AI-Powered Deep CNN Models for Automated Classification of Multiple Cancer Types

Still cancer remains one of the main causes of death worldwide, hence its identification presents a great difficulty for doctors and researchers. Early diagnosis is crucial, but traditional methods of cancer detection sometimes rely on invasive surgeries followed by extensive investigation, which emphasizes the necessity of more exact and effective diagnosis options. With an eye on deep learning (DL) representations, this learning tackles these objectives by using AI-driven approaches for automated cancer diagnosis. datasets from (7) different cancer types— pelvic, lung, colon, leukemia with acute lymphocytic disease, breast, the renal system, brain, and oral cancer—allow evaluation of pre trained model, and a suggested fine-tuned VGG16. Segmenting an image starts the process; next is contour feature mining, in which measurements of extent that epsilon, then boundary is derived. With the lowest Root Mean Square Error (0.03 for training with 0.04 for validation), the suggested Finetuned the VGG-16 surpasses the others, earning the lowest validation accuracy (98%), and the lowest loss (0.001). These results highlight the model’s success and potential for improving accuracy and effectiveness in malignancy detection.