Multiple Types of Cancer Classification Using Histopathological Images Based on Learning Without Forgetting Powered Deep Learning Models

Multiple Types of Cancer Classification Using Histopathological Images Based on Learning Without Forgetting Powered Deep Learning Models

Kavin M, Srivanth S, Siva Senthil D, Sri Vinitha V

CSE Department,  Bannari amman institute technology

Erode, India

Abstract—Cancer detection represents a paramount challenge in modern medicine, where the ability to accurately identify malignancies across various anatomical regions can profoundly impact patient outcomes. In this study, we introduce an innovative approach for the simultaneous detection of brain, lung, and breast cancers using histopathological images, which are rich in cellular information critical for diagnosis. Through the utilization of Convolutional Neural Networks (CNNs), our system achieves remarkable accuracy in distinguishing cancerous tissues amidst complex histological backgrounds. The seamless integration of CNN models with Flask for backend deployment and HTML, CSS, and Python for frontend web development ensures a user-friendly interface conducive to both healthcare professionals and patients. Extensive validation of our methodology, conducted on diverse datasets encompassing a spectrum of cancer types and stages, underscores its robustness and reliability. The results obtained showcase not only the high accuracy of our system in detecting individual cancer types but also its versatility in concurrently predicting multiple malignancies. Furthermore, our approach demonstrates promising generalization capabilities, indicating its potential applicability across various healthcare settings and patient populations. By harnessing the power of advanced machine learning techniques, our research represents a significant leap forward in the realm of cancer diagnostics. The implementation of our methodology has the potential to revolutionize clinical practice by enabling earlier detection, personalized treatment planning, and improved patient outcomes.