Convolutional Neural Networks for Medical Image Diagnosis and Prognosis

  • Parameswari A Department of ECE, Adithya Institute of Technology, Coimbatore, India
  • Vinoth Kumar K Department of ECE, SSM Institute of Engineering and Technology, Dindigul, India
Keywords: Convolutional neural networks, Deep neural networks, K- Mean Clustering


One of the most incredible machine learning methods is deep learning. Utilised for picture categorization, clinical archiving, item identification, and other purposes. The quantity of medical image archives is expanding at an alarming rate as hospitals employ digital photos for documentation more frequently. Digital imaging is essential for assessing the severity of a patient's illness. Medical imaging has a wide variety of uses in research and diagnostics. Due to recent developments in image processing technology, self-operating identification of medical photos is still a research area for computer vision researchers. We require an appropriate classifier in order to categorise medical photos using various classifiers. After organ prediction and classification, the research was modified to include medical picture recognition. For medical picture detection, pretrained convolutional networks and Kmean clustering techniques similar to those used for organ identification are employed. Separating the training from the test data allowed for the data's authentication. The application of this strategy has been proven to be most effective for categorising various medical images of human organs.


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How to Cite
A, P., & K, V. K. (2022). Convolutional Neural Networks for Medical Image Diagnosis and Prognosis. International Journal of Computer Communication and Informatics, 4(2), 54-62.

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