# DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 38
Subclasses

Abdurrahim Yilmaz1
Sirin Pekcan Yasar2
Gulsum Gencoglan3,†
Burak Temelkuran1,†

###### Abstract

Skin lesion datasets provide essential information for understanding various
skin conditions and developing effective diagnostic tools. They aid the
artificial intelligence-based early detection of skin cancer, facilitate
treatment planning, and contribute to medical education and research.
Published large datasets have partially coverage the subclassifications of the
skin lesions. This limitation highlights the need for more expansive and
varied datasets to reduce false predictions and help improve the failure
analysis for skin lesions. This study presents a diverse dataset comprising
12,345 dermatoscopic images with 38 subclasses of skin lesions collected in
Türkiye which comprises different skin types in the transition zone between
Europe and Asia. Each subgroup contains high-resolution photos and expert
annotations, providing a strong and reliable basis for future research. The
detailed analysis of each subgroup provided in this study facilitates targeted
research endeavors and enhances the depth of understanding regarding the skin
lesions. This dataset distinguishes itself through a diverse structure with 5
super classes, 15 main classes, 38 subclasses and its 12,345 high-resolution
dermatoscopic images.

1\. Department of Metabolism, Digestion, and Reproduction, Imperial College
London, London, SW7 2AZ, United Kingdom 2\. Department of Dermatology, The
University of Health Sciences, Haydarpasa Numune Research and Training
Hospital, Istanbul, 34668, Turkiye 3\. Department of Dermatology, Istinye
University, Liv Hospital Vadistanbul, Istanbul, 34010, Turkiye Corresponding
authors: Gulsum Gencoglan (ggencoglan@gmail.com), Burak Temelkuran
<EMAIL_ADDRESS>
_K_ eywords Dermatoscopy $\cdot$ Skin Lesion Dataset $\cdot$ Skin Cancer
$\cdot$ Subclasses of Skin Lesions $\cdot$ Artificial Intelligence $\cdot$
Deep Learning

## Background & Summary

Dermatoscopy, also known as epiluminescence microscopy, is the inspection of
the skin using magnifying lenses and polarized or non-polarized filtered
illumination, allowing examination of lesions invisible to the naked eye. It
is the most extensively used and standardized diagnostic procedure in clinics
to diagnose skin lesions. Various types of dermatoscopes are actively used in
daily clinics. Hand-held dermatoscopes, for example, have become widely
available, affordable, and widely employed, especially in developing
countries. However, dermatologists’ diagnostic performance in dermatoscopic
skin lesion identification is closely linked to their training and prior
clinical experience, highlighting a variability inherent in human decisions.
The prevalence and structure of malignant and benign lesions that vary
depending on race, geographical factors and skin type also affects
dermatologists’ performance. Addressing this gap, artificial intelligence (AI)
trained on specialized dataset can reduce the experience gap that results from
the human factor during the clinical examination of these lesions [1].

One of the initial studies was published on the classification of only three
classes: melanoma, nevus, and seborrheic keratosis [2, 3]. The skin lesion
dataset HAM10000, which had seven classes and common lesions, was published in
2018 [4]. Eight classes with the squamous cell carcinoma skin lesion which is
a common lesion but uncovered in HAM10000 was later presented by the BCN20000
dataset [5]. Among dermatoscopic skin lesion datasets, BCN20000 has the
largest number of multi-class images (eight classes), still requires further
development and expansion to include subclasses. New datasets are continued to
be published in open access repositories such as The International Skin
Imaging Collaboration (ISIC), which currently has a total of 76,108 public
images (accessed at: 30 May 2024) [6, 7]. Publishing new datasets enables the
development of more robust and intelligent AI algorithms with different
approaches.

Datasets on skin lesions can be listed in four different categories: clinical,
pathological slide, and dermatoscopic image datasets, and datasets combining
more than one of these modalities. Clinical image datasets such as the
Interactive Atlas of Dermatoscopy and the Dermofit Image Library can be
accessible for a fee [8, 9]. The MED-NODE Dataset [10], the Asan and Hallym
Dataset [11], the SD-198/SD-260 Datasets [12, 13, 14], PAD-UFES-20 [15], SCIN
(5.2% skin lesion) [16], Atlas Dermatologica [17], and DermaAmin [18] are all
open-access. Fitzpatrick 17k Dataset is a dataset sourced from Atlas
Dermatologica and DermaAmin, with the addition of Fitzpatrick skin type
information (28.62% skin lesion) [19]. Diverse dermatology images (DDI)
dataset also presents skin images with their skin type [20]. Cancer Genome
Atlas is a pathological slide image dataset for skin lesions in the literature
[21]. Clinical, dermatoscopic, and pathological images can all be found on
Dermnet NZ for a free (high-resolution images for a fee) [22]. Study results
show that the diagnostic accuracy (by clinicians and AI) achievable using
dermatoscopic image datasets result in higher success rates when compared to
those achievable using clinical image datasets, bringing out the importance of
the dermatoscopic image datasets [23]. Pathological images require tissue
excision from the patient. For that reason, pathological image dataset is not
as practical as dermatoscopic observation.

PH2 dataset was published for benchmarking dermatoscopic images including with
three lesion classes (common nevus, atypical nevus and melanoma (mel)) [24].
The Derm7pt dataset with five lesion classes (basal cell carcinoma (bcc), mel,
miscellaneous, nevus (nv), and seborrheic keratosis (sk)), 2000 dermatoscopic
and clinical images was published along with the 7-point checklist pattern
analysis and its automation by using artificial neural networks [25]. The
largest dermatoscopic dataset included in the scientific studies is the
International Skin Imaging Collaboration (ISIC) archive with 240,000+ total
images (accessed at: 30 May 2024). This archive contains these datasets:
ISIC-2016 with 1,279 images and two lesion classes (benign and malignant) [2],
ISIC-2017 with 2,000 images three lesion classes (mel, nv, and sk) [3],
ISIC-2018 [26] and HAM10000 with 10,015 images and seven lesion classes
(akiec, bcc, benign-keratosis like (bkl), dermatofibroma (df), mel, nv, and
vascular (vasc)) [4], BCN20000 with 19,424 images and eight lesion classes
(actinic keratosis (ak), bcc, df, mel, nv, squamous cell carcinoma (scc), sk,
and vasc) [5] and Patient-Centric dataset with 33,126 images and two lesion
classes (benign and mel) [27]. Lastly, the dataset collected in Argentina with
10 subclasses (ak, bcc, df, mel, nv, scc, sk, solar lentigo (sl), lichenoid
keratosis (lk), and vasc) was added into ISIC dataset [6]. Between 2016 and
2020, numerous competitions were held on challenges such as lesion
segmentation, feature extraction, and lesion classification, using these ISIC
datasets.

These large datasets are crucial for the development of AI based models for
skin lesion classification purposes. In addition, the introduction of subclass
annotations in skin image datasets has high potential to enhance studies in
the AI field focusing on skin disease, for creation of more trustworthy,
robust, and intelligent systems. Here, our study presents a taxonomic tree for
skin lesion classes and a dataset of 38 skin lesions annotated with subclasses
for the first time in the scientific literature. This dataset, which contains
a total of 12,345 images, is a comprehensive dermatoscopic image dataset
available. Moreover, this is the one of the largest collection to date in
terms of the total number of images in multiclass datasets.

## Methods

The DERM38 dataset contains 12,345 high-resolution dermatoscopic images from
1,761 patients, which were collected from 2008 to 2020 in the Department of
Dermatology and Venerology at Celal Bayar University (Manisa, Türkiye),
Istinye University (Istanbul, Türkiye) and University of Health Sciences
Haydarpasa Numune Research and Training Hospital (Istanbul, Türkiye) by using
MoleMax 3, MoleMax HD (Derma Medical Systems, Vienna, Austria), FotoFinder®
videodermatoscope (FotoFinder Systems, Bad Birnbach, Germany) and 3gen
Dermlite DL4 hand-held dermatoscopes (DermLite LLC, California, United States
of America) with connection kit for iPhone 5 and 7 (Apple Inc., California,
United States of America). Ethical approval of images was based on ethics
review board protocols 20.478.486/1023 (Manisa Celal Bayar University,
24/11/2021). The informed consent was waived because of the retrospective
nature of the study as the dataset contains anonymized data. The final dataset
includes 38 skin lesion classes. The collection process includes capturing a
wide variety of skin lesions using both digital dermatoscopy devices and high-
resolution digital single-lens reflex (DSLR) cameras with dermatoscopy
attachment. The goal is to improve a diverse representation of data in terms
of image quality, magnification, and resolution. The overview of data
collection is shown in Fig. 1.

Figure 1: Shows an overview of the data collection procedure.

### Data collection from digital dermatoscopy devices

Digital dermatoscopy devices are computer-attached systems and outfitted with
dermatoscopes that utilize polarized light, enabling a magnification such as
from 10x to 140x. This feature facilitates the thorough examination of deeper
skin structures with a high level of detail. These dermatoscopy devices are
linked to a computer system equipped with specialized software for the purpose
of capturing and storing images. By using this software, dermatologists can
follow up on the patients and their skin lesions with detailed information
such as location and lesion classes. Each lesion is captured using a
standardized technique and consistent lighting conditions to minimize any
potential variability. Dermatoscopy is performed in the contact-polarized mode
using an interface medium to avoid reflection caused by excessive scales, with
ultrasound gel applied when necessary. The dermatologists ensure the most
possible and accurate alignment of the field of view, with the lesion at the
center and a small margin of surrounding healthy skin included to provide
contextual information. To generate our dataset, we exported the data using
their software tools in suitable formats such as HTML files. The raw data was
subsequently retrieved from the raw files as lesion images and their metadata
information. The cases were selected from this system by two expert
dermatologists according to their consensus benign diagnosis, follow-up, and
excised lesions with a histopathologic report.

### Data collection from mobile phones and hand-held dermatoscopes

Mobile phone integrated dermatoscopes are low-cost and accessible devices that
can provide state-of-art image quality. In this dataset, Apple iPhone 5 and 7,
equipped with a 3gen Dermlite DL 4 hand-held dermatoscope kits, were used to
collect dermatoscopic images. The Dermlite DL4 attachment with mobile devices
enables the utilization of polarized light dermatoscopy, which offers a
maximum magnification of 10x. The use of high-level magnification is essential
for thoroughly analyzing the intricate components of the skin. The mobile
phone was utilized alongside dermatoscope attachments, facilitating the
acquisition of high-resolution (such as at 3840x2160 pixels, 4K resolution)
skin lesion photos directly onto the phone. The mobile photos, along with
their metadata, including lesion classes, were systematically documented, and
matched with their records on digital dermatoscopy devices. A standardized
image acquisition process was adhered to keep consistent lighting and framing
conditions for all photos. The dermatologists conducted the imaging by
positioning the lesions in the center of the field of view as possible while
also including a border of healthy skin to offer contextual information. When
focusing or centering the mobile phone camera was challenging, the
dermatologists took photos in different angles. This usually caused the edges
of the dermatoscopes as a large black region to be visible on these images.
These black regions were cropped from the images meticulously to increase
standardization of images.

### Data selection and quality control

The raw dataset included not only dermatoscopic images but also non-
dermatoscopic images and other images such as device test images. To
accelerate the data selection procedure, the dermatoscopic images were
extracted by utilizing a script in Python (version: 3.11.5) that selected the
images together with their metadata information. The BeautifulSoup library
(version: 4.12.2) was utilized for the automated extraction and categorization
of photos in HTML format. Data that could not be extracted or exported
automatically in HTML format was hand-labeled and classified. The metadata was
additionally stored manually in comma-separated values (CSV) files. To remove
duplicate images, their file sizes were compared by using a script coded in
Python. These scripts found 280 duplicate images. Then, they were manually
reviewed. Consequently, all dermatoscopic images were retrieved and their
corresponding information was stored in a CSV file. After automated extraction
of the images, the careful selection of data was facilitated to maintain the
integrity and usefulness of the dataset for both clinical reference and
computational purposes. The criteria for selection were determined prior to
the initiation of data collection and were consistently followed during the
selection procedure.

Inclusion Criteria

  * •

Image Quality: Images must be clear, in focus, and have sufficient quality.
All images were reviewed manually to ensure that key diagnostic features of
the skin lesions are understandable.

  * •

Diagnostic Confirmation: Only images of lesions with a consensus of clinical
diagnosis by two expert dermatologists or a histopathological confirmation
(where available) were included.

Exclusion Criteria

  * •

Poor Image Quality: Images that were blurred, under- or over-exposed, or had
large artifacts. All images were reviewed manually, and the images that could
potentially interfere with diagnosis were excluded.

  * •

Incomplete Data: Images lacking essential metadata such as the presumed
diagnosis were not included in the dataset.

  * •

Ethical Concerns: Images that contained identifiable patient information or
did not have proper consent were excluded to uphold ethical standards.

  * •

Unsuitable: Images which contain unsuitable skin diseases or artefacts were
excluded. Nail lesions, mucosal lesions, cockade nevus, leukoplakia,
sarcoidosis psoriasis, verruca, cyst, keloid, sebaceous hyperplasia, wart,
genital wart, lipoid proteinoz, subcorneal hemorrhage, pigmented purpuric
dermatosis, mucosal melanosis, pygmented bowen, verruca, sarkoidoz psoriasis,
and some types of adenocarcinoma such as sebaceous carcinoma and eccrine
glands were excluded.

Selection Process The selection process involved multiple stages:

  * •

Initial Screening: A preliminary review of images was conducted by trained
engineers to remove images that obviously did not meet the inclusion and
exclusion criteria. If one more than different classes of skin lesion is
located in an image, these images are cropped.

  * •

Expert Review: Two expert dermatologists then reviewed the remaining images to
ensure that they met the clinical standards and diagnostic requirements.

  * •

Quality Assurance: A final review was conducted by a panel comprising two
dermatologists (GG and SPY with over 20 years experience on dermatoscopy) to
ensure the images were of suitable quality for both clinical reference and
computational tasks.

Table 1: The DERM38 dataset contains 12,345 skin lesions divided into 5 super classes, 15 main classes, and 38 subclasses. Superclass | Mainclass | Subclass | # of Images
---|---|---|---
Melanocytic Benign | Banal Compound | Acral (an) | 22
Congenital (con) | 423
Miescher (mn) | 38
Nevus (cn) | 673
Banal Dermal | Blue (bn) | 156
Nevus (dn) | 539
Banal Junctional | Acral (an) | 351
Congenital (con) | 145
Nevus (jn) | 1419
Lentigo | Ink Spot Lentigo (isl) | 6
Lentigo Simplex (ls) | 23
Solar Lentigo (sl) | 65
Dysplastic Compound | Acral (an) | 8
Congenital (con) | 30
Nevus (cn) | 426
Dysplastic Junctional | Acral (an) | 212
Spitz (sn) | 10
Nevus (jn) | 5465
Dysplastic Recurrent | Recurrent (rn) | 15
Melanocytic Malignant | Melanoma | Acral Nodular (anm) | 78
Acral Lentiginious (alm) | 54
Lentigo Maligna (lm) | 85
Lentigo Maligna Melanom (lmm) | 21
Melanoma (mel) | 164
Nonmelanocytic Benign | Keratinocytic | Seborrheic Keratosis (sk) | 632
Lichenoid Keratosis (lk) | 6
Skin Appendages | Dermatofibroma (df) | 173
Vascular | Hemangioma (ha) | 269
Lymphangioma (la) | 10
Pyogenic Granuloma (pg) | 7
Nonmelanocytic Indeterminate | Keratinocytic | Actinic Keratosis (ak) | 48
Nonmelanocytic Malignant | Keratinocytic | Basal Cell Carcinoma (bcc) | 423
Bowen’s Disease (bd) | 46
Cutaneous Horn (ch) | 9
Mammary Paget Disease (mpd) | 13
Squamous Cell Carcinoma (scc) | 263
Skin Appendages | Dermatofibrosarcoma Protuberans (dfsp) | 4
Vascular | Kaposi Sarcoma (ks) | 14
Total | 12,345

## Data Records

All images are in JPEG format with their corresponding label in metadata. The
metadata file was generated in CSV file to process easily. The metadata
contains file names and lesion classes with their detailed taxonomic
identification.

A three-level taxonomy tree following the latest dermatological classification
standards [28] was created to guide the use of DERM38.The benign skin lesions
that are high risk of being or becoming malignant were especially classified
into a separate subclass such as dysplastic nevus [29], congenital nevus [30],
blue nevus [31] and recurrent nevus that have the risk of unremoved malignant
cell. The skin lesions were classified according to their specific anatomical
localizations such as palm-sole (acral), face and trunk-extremities. Combined
lesions with multiple pathological classification were excluded if cropping
into separate images is not possible. Skin lesions were initially grouped into
the two classes as melanocytic and non-melanocytic. These two classes were
further divided into malignant, indeterminate and benign groups, and five
super classes were created. These five super classes were then divided into 15
main classes. Lastly, sub-lesion types were then classified into 38 different
subclasses. Melanocytic-benign lesions were classified into lentigo with ink
spot lentigo (isl), lentigo simplex (ls), and solar lentigo (sl) subclasses,
banal with dermal, compound, and junctional subclasses and dysplastic with
compound, junctional, and recurrent (rn) subclasses. Dysplastic dermal nevus
were added into banal dermal nevus because of their similar patterns. Common
nevus subclasses (acral (an), blue (bn), Miescher (mn), congenital (cn), and
spitz/reed (sn) nevus) under dermal, compound, and junctional groups were then
classified into their subclasses if available. Melanoma (mel) was classified
under melanocytic-malignant lesions, with five further subclasses as acral
nodular melanoma (anm), acral lentiginous melanoma (alm), lentigo maligna
(lm), lentigo maligna melanoma (lmm), and melanoma (mel). Nonmelanocytic-
benign lesions were classified into two subclasses as keratinocytes (lichenoid
keratosis (lk), seborrheic keratosis (sk)), skin appendages (dermatofibroma
(df)), and vascular (hemangioma (ha), lymphangioma (la), and pyogenic
granuloma (pg)). Nonmelanocytic-malignant lesions were classified into three
subclasses as keratinocytes (basal cell carcinoma (bcc), Bowen’s disease (bd),
cutaneous horn (ch), mammary Paget disease (mpd), and squamous cell carcinoma
(scc)), skin appendages (dermatofibrosarcoma protuberans (dfsp)), and vascular
(Kaposi sarcoma (ks)). In addition, actinic keratosis (ak) was classified
under nonmelanocytic-indeterminate subclasses. The taxonomy tree of these
classes and their sizes were shown in Fig. 2 and Table 1, respectively.

## Technical Validation

All images in this dataset were of patients applying to the dermatology
department with skin complaints. All lesions were classified and reviewed
according to the rules of dermatoscopic evaluation by two expert
dermatologists in a consensus decision. All malignant skin lesions were all
biopsy proven. The majority of these lesions consisted of follow-up images and
were labeled using patient records. Nevus with over 2 years of digital
dermatoscopic follow-up were annotated with no change except nevoid
involution. The number of dysplastic nevus (N: 6,157) constituted a large part
of the dataset. These dysplastic nevi were followed up in daily clinical
practice rather than banal nevus (N:3,764). Malignant forms of benign lesions
such as dfsp (N:4, counterpart of df) were also included in the dataset, even
if the number of data is small. Early forms of malignant tumors such as mpd
and bd which are a type of intraepidermal scc were also included. The
consensus of GG and SPY experts labeled all the benign images that did not
have histology or further follow-up. GG (with second Ph.D. in Basic Oncology)
and SPY are two expert dermatologists with 20+ years of experience in
dermatology.

## Usage Notes

The dataset provided in this study represents the initial comprehensive
collection of skin lesion data from Türkiye, serving as a distinctive and
important asset for both the medical and machine learning fields. The launch
of this dataset represents a notable progression, especially when considering
the wide range of skin types, it addresses and the comprehensive
classification of skin lesions it contains. The dataset is valuable due to the
inclusion of various benign subclasses that closely resemble malignant tumors,
distinguishing it from current collections and presenting a novel opportunity
for the advancement of advanced diagnostic algorithms.

The absence of a rich sub-classification may result in misclassification. For
instance, congenital nevus (which is not categorized as a separate subclass in
the published datasets) can often be mistaken as a melanoma [30]. It is also
important to know that the lesion is a congenital nevus as this lesion has a
high risk of evolving into a melanoma [30]. We also included uncommon lesions
that are difficult to collect or annotate such as spitz/reed nevus [32]. By
using our detailed subclassification, this dataset enables researchers to
develop and refine more reliable AI models capable of distinguishing benign
from malignant ones, improving diagnostic accuracy and reducing the likelihood
of misdiagnosis. The dataset includes 38 subclasses, allowing researchers to
focus on more intelligent algorithms such as hierarchical learning [33].

The dataset exhibits potential to serve as a fundamental component in the
advancement of effective algorithms for identifying malignant skin lesions.
The ongoing obstacle in dermatological diagnosis lies in the difficulty of
differentiating between benign and malignant forms, particularly when they
exhibit comparable visual characteristics. The incorporation of benign that
exhibit similarities to malignant counterparts in this collection of data
presents a potential for enhancing the precision of machine learning
algorithms. Therefore, it is anticipated that this technology will facilitate
progress in the early and accurate detection of diseases, potentially
mitigating the occurrence of false positive results and ultimately enhancing
patient prognoses.

The inclusion of the dataset originating from Türkiye contributes to the
expansion of the worldwide data repository, which has historically been
underrepresented in this research domain. The geographical specificity of this
information is of great use to academics who seek to create diagnostic models
that are both resilient and efficient across various ethnicities and
geographic areas. Additionally, it functions as a valuable resource for
doctors who aim to comprehend the diversity in lesion, which can be impacted
by various factors such as regional environmental conditions and genetic
predispositions.

Figure 2: Shows an overview of the taxonomy tree. The first level includes the
melanocytic and nonmelanocytic. The second level comprises malignant and
benign groups of the first level. The third level is banal, dysplastic and
lentigo for melanocytic benign, melanoma for melanocytic malignant,
keratinocytic, skin appendages, vascular for nonmelanocytic benign,
keratinocytic for nonmelanocytic-indeterminate, and keratinocytic, skin
appendages, and vascular for nonmelanocytic malignant. The forth level is the
subclasses of related to their main and super classes. Our taxonomy tree
contains 5 super classes, 15 main classes, and 38 subclasses.

Clinicians are encouraged to employ this dataset as a visual resource for the
identification of skin lesions, while researchers are encouraged to utilize it
for comparative analyses with other datasets specific to the location. This
dataset is of particular utility to machine learning practitioners for the
purposes of training and verifying classification algorithms. The
comprehensive depiction of complex instances within the dataset offers a
rigorous platform for evaluating algorithms intended to distinguish between
benign and malignant tumors.

In brief, this dataset serves to enhance the existing body of data pertaining
to skin lesions, while also paving the way for the development of
sophisticated diagnostic instruments. We anticipate that this tool will be a
helpful resource in enhancing the precision and effectiveness of skin cancer
detection and diagnosis among various demographic groups.

In this dataset, there are 38 subgroup lesions. Brief descriptions of these
subgroups presented below can be useful for AI researchers:

I. Melanocytic lesions: Originates from melanocytes, often pigmented, shades
of brown, black, tan, blue. Reflecting varying concentrations and depth of
melanin within the skin.

  1. 1.

Benign: Non-cancerous melanocyte-origin moles, generally harmless.

    1. (a)

Dysplastic Nevus: Also known as atypical or Clark’s nevus. They are
characterized by histologic features, and they may appear small and banal
clinically. Dysplastic nevi demonstrate the following clinical features:
usually > 5 mm, irregular borders, some with a pigmented and erythematous rim,
and variegated pigmentation with a mixture of pink, light and dark brown
colors [34, 35]. This subclass contains compound nevus with acral and
congenital subclasses, junctional nevus with acral and spitz/reed subclasses,
and recurrent nevus.

    2. (b)

Banal Nevus: A banal nevus, commonly known as a common mole, is a typical,
non-cancerous skin mole that usually appears as a small, regular, round, or
oval spot. Brown, tan, or pink, and may be either flat or slightly raised.
This subclass contains dermal nevus with blue subclass, compound nevus with
acral, Miescher, and congenital subclasses, and junctional nevus with acral
and spitz/reed subclasses.

    3. (c)

Lentigo: Pigmented and uniform. It is related to increasing of melanin
expression by melanocytes. This subclass contains ink spot lentigo, lentigo
simplex, and solar lentigo.

      1. i.

Compound (cn): Common mole, partially flat and partially elevated. Mix of
dermal and epidermal melanocytes.

      2. ii.

Dermal (dn): Common mole. Raised bump, flesh-colored or brown mole. Located
within the dermis. Generally benign. Unna nevus is included.

      3. iii.

Junctional (jn): Flat, typically dark, or brown mole. Occurs at epidermis-
dermis junction.

      4. iv.

Recurrent nevus (rn): Incompletely removed nevus. Mole regrowth at previously
excised site. Typically, benign. Can be confused with melanoma.

        1. A.

Acral nevus (an): Mole on palms or soles. Usually benign and characterized by
their distinctive location on the body where the skin is thicker.

        2. B.

Blue nevus (bn): Deeply pigmented mole, blue-black color. Originates from deep
dermal melanocytes. Usually, benign.

        3. C.

Congenital nevus (con): Mole present at birth and can grow proportionally with
the child. Varies in size; potentially large. Increased melanoma risk later in
life. Nevus spilus is included.

        4. D.

Ink Spot Lentigo (isl): Small, dark brown to black spots, resembling ink
spots.

        5. E.

Lentigo Simplex (ls): Small, a precursor to junctional nevus. Small, flat, and
typically darker spot. Also known as simple lentigo.

        6. F.

Miescher nevus (mn): Reveal pseudo network around hair follicles. Commonly
benign, typically brown.

        7. G.

Solar Lentigo (sl): Larger, a precursor to seborrheic keratosis. Sun exposed.

        8. H.

Spitz/Reed nevus (sn): Raised, pink, red or brown mole. Often mistaken for
melanoma. Typically, benign in children. Sometimes, shows a starburst pattern.

  2. 2.

Malign: Cancerous growth from melanocytes. High risk of spreading.

    1. (a)

Melanoma: Aggressive skin cancer. Arises from melanocytes. High risk of
metastasis.

      1. i.

Acral nodular melanoma (anm): Nodular melanoma form on poles and soles.

      2. ii.

Acral lentiginous melanoma (alm): A rare subtype of melanoma. Lentiginous form
on poles and soles.

      3. iii.

Lentigo maligna (lm): Larger, a precursor to lentigo maligna melanoma. Slow-
growing skin cancer. Appears as a flat, blotchy patch. Early form of melanoma.
Located on sun-exposed areas of the skin.

      4. iv.

Lentigo maligna melanoma (lmm): Various number of colors, especially blue or
black that means the melanoma cells have reached to the deeper layers of skin.

      5. v.

Melanoma (mel): Melanoma can appear as a new dark spot on the skin or from an
existing mole that changes in color, size, or feel. It can spread quickly to
other parts of the body and is critical to treat early.

II. Nonmelanocytic lesions: Uncontrollable growth pattern from keratinocytes,
fibroblasts, or vascular cells.

  1. 1.

Benign: Non-cancerous growths from keratinocytes, fibroblasts, or vascular
cells.

    1. (a)

Keratinocytic:

      1. i.

Seborrheic keratosis (sk): Appears as a waxy, wart-like, often brown or black
growth. Commonly has a rough texture. Stucco keratosis is included which is
located on legs.

      2. ii.

Lichenoid keratosis (lk): Inflammatory and regressing on solar lentigo and
seborrheic keratosis.

    2. (b)

Skin appendages:

      1. i.

Dermatofibroma (df): Firm, benign skin nodule. Common on legs or arms.
Typically, brownish, and harmless.

    3. (c)

Vascular:

      1. i.

Hemangioma (ha): Bright red, raised birthmark. Made of clustered blood
vessels. Common in infants, usually fades.

      2. ii.

Lymphangioma (la): Rare, soft, often translucent mass. Appears as a swelling
on the skin or mucous membranes. Typically seen in infants.

      3. iii.

Pyogenic granuloma (pg): Small, reddish, raised lesion. Prone to bleeding and
rapid growth. Often appears after skin injury.

  2. 2.

Indeterminate: Not exactly categorized as benign or malignant. This does not
imply that these lesions are malignant.

    1. (a)

Keratinocytic:

      1. i.

Actinic keratosis (ak): Rough, scaly skin patch. Sun-induced, precancerous.
Common on sun-exposed areas. 20% malignancy risk.

  3. 3.

Malign: Cancerous growths, and can originate from keratinocytes, vascular, or
connective tissue cells.

    1. (a)

Keratinocytic:

      1. i.

Basal cell carcinoma (bcc): Appears as a shiny, pearly bump or a flat, scar-
like lesion. Most common on sun-exposed areas. Slow growing rarely
metastasizes.

      2. ii.

Bowen’s disease (bd): Early form of scc. Appears as a persistent, red, scaly
patch. Can resemble eczema or psoriasis. Often occurs on sun-exposed skin.

      3. iii.

Cutaneous horn (ch): Hard, protruding growth resembling an animal horn.
Composed of compacted keratin mostly located on the face, ears, and other sun
exposed areas. It may be associated with benign, premalignant, and malignant
lesions. Often benign, base may have underlying malignancy. Only early form of
scc is included.

      4. iv.

Mammary Paget’s Disease (mpd): Early form of scc and a type of breast cancer.
mpd need to be diagnosis before it converts to nodular form.

      5. v.

Squamous cell carcinoma (scc): Common, appears as a firm, red nodule, or a
flat lesion with a scaly, crusted surface. Commonly develops in sun-exposed
areas. Can be ulcerative and may bleed.

    2. (b)

Skin appendages:

      1. i.

Dermatofibrosarcoma protuberans (dfsp): Rare, appears as a deep-seated, firm
lump under the skin. Often starts as a small, painless nodule that gradually
enlarges. Typically develops on the trunk or limbs.

    3. (c)

Vascular:

      1. i.

Kaposi sarcoma (ks): Presents as purple, red, or brown blotches or tumors on
the skin. It is originated from vessel’s endothelial cells.

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## Acknowledgements

Abdurrahim Yilmaz has been funded by the President’s PhD Scholarships of
Imperial College London.

All figures are created with BioRender.com.

## Author contributions statement

Conceptualization, A.Y., S.P.Y., and G.G.; methodology, A.Y., G.G., and B.T.;
software, A.Y.; validation, G.G., S.P.Y., and B.T.; formal analysis, A.Y.;
investigation, G.G. and S.P.Y.; resources, G.G. and S.P.Y.; data curation,
A.Y., G.G., S.P.Y., and B.T.; writing—original draft preparation, A.Y., and
B.T.; writing—review and editing, A.Y. and G.G.; visualization, A.Y. All
authors have read and agreed to the published version of the manuscript.

## Competing interests

There is no competing interest.