• Himanshu Srivastava
• Drishti Tiwari
• Prakhar Tripathi
• Ramhit Sharma
• Dr. Nikhat Akhtar

Cancer detection has always been a fundamental focus in oncological research. Recent years have seen a significant advancement in the revolution of Machine Learning (ML) and its use in oncology. This study covers the design and implementation of a Multiple Cancer Detection System using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The main aim of this system is to provide a precise and effective approach for the early diagnosis of diverse cancer forms, using breakthroughs in machine learning and image processing. The method employs CNNs for feature extraction from medical imaging data, facilitating the identification of complex patterns linked to malignant tissues. The collected characteristics are further identified using Support Vector Machine (SVM), recognized for its efficacy in managing high-dimensional data, to ascertain the existence and kind of malignancy. Our model was trained and assessed using public ally accessible datasets including imaging data for several cancer types, including lung, breast, and skin cancer. The system attained notable accuracy rates in identifying malignant situations, illustrating the efficacy of integrating CNNs with SVM for medical diagnostics. The suggested system may aid medical practitioners by offering a dependable diagnostic tool that minimizes human errors and expedites the decision-making process.

Data-Driven, Convolutional Neural Networks (CNN), Healthcare, Cancer Detection, Disease Prediction, Support Vector Machine (SVM), Machine Learning.

" A Data-Driven Approach to Multi-Cancer Detection Using Machines Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.f181-f189, May-2025, Available :http://www.jetir.org/papers/JETIR2505617.pdf

" A Data-Driven Approach to Multi-Cancer Detection Using Machines Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppf181-f189, May-2025, Available at : http://www.jetir.org/papers/JETIR2505617.pdf