A Comprehensive Analysis of Convolutional Neural Networks for Medical Image Classification and Segmentation Tasks

The deployment of Convolutional Neural Networks (CNNs) in medical image analysis has seen significant advancements, particularly in the precise segmentation and classification of medical images. This paper reviews the effectiveness of specific CNN architectures, notably U-Net and Inception, which have demonstrated high accuracy and efficiency on key medical imaging datasets including BraTS, ChestX-ray8, and ISIC 2017. The impact of these CNNs extends beyond technical performance, contributing substantially to the improvement of medical diagnostics and patient care. U-Net has notably enhanced tumor segmentation, thereby aiding in quicker and more accurate treatment planning. Similarly, Inception has been instrumental in the early detection of skin cancers, which is crucial for timely therapeutic intervention. The integration of CNNs into medical diagnostics not only streamlines clinical workflows but also promotes a shift towards more predictive and preventative healthcare models. The paper concludes that continuous advancements in CNN technology are crucial for the future of healthcare, necessitating further research focused on addressing challenges related to data scarcity, class imbalance, and model interpretability to fully optimize the use of CNNs in medical applications.