Medical Image Classification and Cancer Detection using Deep Convolutional Neural Networks Akshay Kumar S Department of Computer Science College of Engineering Perumon Perinad P O, Kollam, Kerala Karthika A J Department of Computer Science College of Engineering Perumon Perinad P O, Kollam, Kerala Allen Saju N Department of Computer Science College of Engineering Perumon Perinad P O, Kollam, Kerala Fida Mohammed Khaleel Department of Computer Science College of Engineering Perumon Perinad P O, Kollam, Kerala Gayathri J L PG Scholar Department of Computer Science College of Engineering Perumon Perinad P O, Kollam, Kerala Sujarani M S Assistant Professor (CSE) College of Engineering Perrumon, Perinad P O, Kollam, Kerala Abstract - One of the most startling machine learning approaches is deep learning. It’s utilized in image categoriza- tion, image detection, clinical archives, and object identification, among other things. Medical image archives are growing at an alarming rate, thanks to the widespread use of digital photographs as information in hospitals. Digital images play an important role in predicting the severity of a patient’s disease, and medical images have numerous applications in diagnosis and research. Because of recent advances in imaging technology, automatically classifying medical images is a research problem that is still being worked on by computer vision researchers. Medical image classification according to various classifiers an appropriate classifier will be needed. After organ prediction and classification, the modification of the project was cancer detection. A pre-trained convolutional network and the trans- fer learning process similar to organ detection are used for cancer detection. The validation of this data was done by splitting train and testing data. The conclusion of this method is most suitable for the classification of different medical images of hu-man body organs. Keywords - Medical Image Classification; Cancer Detection; Deep learning; Convolutional Neural Network (CNN); DNN; CBIR Fig. 1. Sample images of medical image dataset 1. INTRODUCTION The spread of digital devices and camera technologies has changedthe exponential development in medical image output. A modern hospital is currently using a computerized picture to anticipate theseverity of the illness of a patient. As digital images evolve rapidly, classification of images has become increasingly useful. Magnetic Resonance Imaging (MRI) seems to be another type of scan that can make and provide accurate information as well as precisely ar-ticulated images of various organs of the body, as well as the brain. International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Published by, www.ijert.org NCREIS - 2021 Conference Proceedings Volume 9, Issue 13 Special Issue - 2021 12