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 .
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DOI:
10.17148/IJARCCE.2023.12321
[1] Vijay Aglawe, Pankaj Phulera, Amar bhade, Divyaamshu Verma, Rupesh Mahajan, "Multiple disease detector using Machine learning and deep learning Techniques," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2023.12321
