International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 108-116, January 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
V Gopi
Sowdepalli Reshma
Nitish Kumar
Yatham Uma Maheshwar Reddy
Chinna Mallanna Gari Siva Nanda Kumar
Vikee Kumar
Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.
Abstract: Early cancer detection plays a crucial role in improving patient survival rates while significantly reducing treatment costs. This research focuses on developing an AI-driven diagnostic system for the early detection of skin, breast, and lung cancers using advanced deep learning techniques. Convolutional Neural Networks (CNNs), along with pre-trained models such as ResNet and DenseNet, are employed for automatic and robust feature extraction from medical images. The system is designed to analyze dermoscopic images for skin cancer, mammograms for breast cancer, and lung CT scans for lung cancer. Publicly available datasets are utilized, with comprehensive preprocessing and data augmentation techniques applied to enhance model generalization and accuracy. The performance of the proposed models is evaluated using standard metrics including accuracy, precision, recall, F1-score, and ROC-AUC. The ultimate objective of this research is to deliver a fast, reliable, and efficient diagnostic support tool that assists clinicians in early cancer detection, thereby improving patient outcomes and advancing AI-assisted healthcare systems.
Keywords: Multi-Disease Detection, Medical Data Classification, Disease Prediction, Medical Imaging Analysis, Feature Extraction.
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