Deep learning methods for cancer recognition
Yiwei Shen · Applied and Computational Engineering · 2024
Modern medical technology has been advancing continuously, and deep learning, a potent machine learning technique, demonstrates significant potential in the domain of cancer identification and diagnosis. This paper through methods of lieterature review, examines the distinctive abilities and advancements of deep learning in the recognition, classification, and segmentation of breast and lung cancer images. Deep learning has proven to be very suitable for medical image analysis and has the ability to aid and even autonomously make decisions in many stages of cancer diagnosis, including history taking, imaging, and biopsy. Furthermore, it possesses the capability to precisely identify, categorize, and divide photos pertaining to breast and lung cancer. Additionally, it has the capacity to identify and predict tumors based on gene expression profiles, so enhancing the efficacy of contemporary medicine in the identification and prevention of cancer. These findings have significant implications for cancer detection and treatment in contemporary healthcare, offering clinicians a potent tool to enhance diagnostic precision and treatment effectiveness.