Multiple disease detector using Machine learning and deep learning Techniques

Abstract- Medical data is becoming increasingly complex, which highlights the need for automated detection systems. In this paper, a system is proposed that utilizes both machine learning and deep learning techniques to accurately detect multiple diseases. The system makes use of a combination of a convolutional neural network (CNN) and a support vector machine (SVM) to train and classify medical data. To detect different diseases, the pre-trained CNN model is fine-tuned, utilizing transfer learning. The proposed system was evaluated on a dataset of medical images, and it achieved an impressive overall accuracy of 95%. This system has the potential to aid medical practitioners in the early detection and diagnosis of multiple diseases. Keywords -Random Forest ,Thyroid ,Diabetes ,Breast cancer ,Future Scope, CNN, XgBoost .