This paper explores the application of neural networks in cancer detection, emphasizing their effectiveness in classifying cancer cells. Cancer is a disease characterized by uncontrolled cell proliferation, which can lead to severe health complications. The study highlights the importance of early and accurate diagnosis for effective treatment. Neural networks, particularly Convolutional Neural Networks (CNNs), have shown significant promise in cancer diagnosis due to their ability to process and analyze complex data patterns. The paper discusses various neural network techniques, including supervised, unsupervised, and reinforcement learning, and their applications in cancer classification. It reviews existing literature, showing that neural networks, especially CNNs, have achieved high accuracy in classifying cancer cells. For instance, CNNs trained on the Invasive Ductal Carcinoma (IDC) dataset demonstrated high precision in identifying malignant cells. The study also presents an architecture for CNNs used in cancer detection, detailing the convolutional layers and pooling operations that enhance the model's ability to recognize patterns in medical images. The results show that CNNs can effectively detect and classify cancer cells, with high accuracy rates. The paper also discusses the use of various neural network models, such as Multilayer Perceptrons (MLPs), Probabilistic Neural Networks (PNNs), and Perceptron networks, in cancer detection, with MLPs achieving the highest accuracy of 97.1%. The study concludes that neural networks, particularly CNNs, are highly effective in cancer detection and classification. They offer a promising solution for improving the accuracy and efficiency of cancer diagnosis. The integration of neural networks with other techniques, such as particle swarm optimization, further enhances their performance in cancer detection. Overall, the research underscores the potential of neural networks in advancing cancer diagnosis and treatment.This paper explores the application of neural networks in cancer detection, emphasizing their effectiveness in classifying cancer cells. Cancer is a disease characterized by uncontrolled cell proliferation, which can lead to severe health complications. The study highlights the importance of early and accurate diagnosis for effective treatment. Neural networks, particularly Convolutional Neural Networks (CNNs), have shown significant promise in cancer diagnosis due to their ability to process and analyze complex data patterns. The paper discusses various neural network techniques, including supervised, unsupervised, and reinforcement learning, and their applications in cancer classification. It reviews existing literature, showing that neural networks, especially CNNs, have achieved high accuracy in classifying cancer cells. For instance, CNNs trained on the Invasive Ductal Carcinoma (IDC) dataset demonstrated high precision in identifying malignant cells. The study also presents an architecture for CNNs used in cancer detection, detailing the convolutional layers and pooling operations that enhance the model's ability to recognize patterns in medical images. The results show that CNNs can effectively detect and classify cancer cells, with high accuracy rates. The paper also discusses the use of various neural network models, such as Multilayer Perceptrons (MLPs), Probabilistic Neural Networks (PNNs), and Perceptron networks, in cancer detection, with MLPs achieving the highest accuracy of 97.1%. The study concludes that neural networks, particularly CNNs, are highly effective in cancer detection and classification. They offer a promising solution for improving the accuracy and efficiency of cancer diagnosis. The integration of neural networks with other techniques, such as particle swarm optimization, further enhances their performance in cancer detection. Overall, the research underscores the potential of neural networks in advancing cancer diagnosis and treatment.