Simple SummaryHistopathological image analysis can be used for the detection of lung and colon cancer by the investigation of microscopic images of tissue samples. Since manual diagnosis takes a long time and is subjected to differing opinions of doctors, automated lung and colon cancer diagnosis becomes necessary. Therefore, the purpose of the study is to develop a transfer learning approach for lung and colon cancer detection on histopathological image analysis. It involves leveraging pre-trained model to analyze histopathological images. In addition, the proposed model uses improved ShuffleNet with deep convolutional recurrent neural network for feature extraction and classification, respectively. Besides, Al-Biruni Earth Radius Optimization and coati optimization algorithm are employed for hyperparameter tuning process. The experimental result analysis of the proposed model on the LC25000 database shows its promising performance on lung and colon cancer diagnosis.AbstractAn early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.