The article "Clinical Application of Neural Network for Cancer Detection" by R. Kishore Kanna et al. explores the use of neural networks in cancer diagnosis and detection. The authors highlight the significance of accurate cancer identification for timely treatment and emphasize the challenges in early cancer detection. They review various neural network technologies, particularly Convolutional Neural Networks (CNNs), which have shown high efficacy in classifying tumor cells. The study uses a dataset of images from invasive ductal carcinoma (IDC) to train and test CNNs, achieving a classification accuracy of 97.1%. The article also discusses different learning techniques, including supervised, unsupervised, and reinforcement learning, and their applications in cancer classification. The authors conclude that neural network technologies, especially CNNs, are highly effective in cancer detection and management, with significant improvements in accuracy and efficiency.The article "Clinical Application of Neural Network for Cancer Detection" by R. Kishore Kanna et al. explores the use of neural networks in cancer diagnosis and detection. The authors highlight the significance of accurate cancer identification for timely treatment and emphasize the challenges in early cancer detection. They review various neural network technologies, particularly Convolutional Neural Networks (CNNs), which have shown high efficacy in classifying tumor cells. The study uses a dataset of images from invasive ductal carcinoma (IDC) to train and test CNNs, achieving a classification accuracy of 97.1%. The article also discusses different learning techniques, including supervised, unsupervised, and reinforcement learning, and their applications in cancer classification. The authors conclude that neural network technologies, especially CNNs, are highly effective in cancer detection and management, with significant improvements in accuracy and efficiency.