Benign and Malignant Cancer Prediction Using Deep Learning and Generating Pathologist Diagnostic Report

The genesis of the proposition to employ deep learning methods for the identification of cancer images stems from the pressing need to augment the diagnosis and treatment of cancer, a significant public health concern worldwide. This inquiry postulates the conception of a mechanism that employs artificial intelligence (AI) to prognosticate the presence of benign and malignant cancer, with a particular emphasis on colon and breast cancer. The proposed mechanism uses the CNN model to attain precise detection of these cancer types from medical images. Furthermore, the study seeks to verify clinical reports concerning the existence of malignant and benign tumors. The research also centers on producing pathologist reports utilizing AI, utilizing the YOLOV5 AI model to diminish diagnostic duration for patients. Additionally, a linear regression AI model is utilized for histopathology image analysis, enabling the classification of benign and malignant cancer. The ultimate objective of this research is to equip pathologists to provide prompt and accurate reports, thereby facilitating informed treatment decisions and reducing cancer morbidity and mortality. The model produced demonstrates specialized expertise in the detection of colon and breast cancer, as it has been trained on a substantial dataset and optimized for accurate classification. The evaluation results underscore the efficacy of the proposed approach, with precision achieving 87%, recall reaching 82%, and a mean average precision at 50% IoU of 88%.These results serve as a testament to the model’s robust performance in accurately identifying both benign and malignant cancer cases.