Especially the medical field which relies a lot on accurate details for diagnoses, programming and automation minimize and mitigate the risk of human error.

We are happy to announce that we have achieved substantially higher metrics than we had anticipated and this has been made possible by using DenseNet Transfer Learning using PyTorch, all of which are very unconventional methods for classification. Our model also surpasses physician capabilities, and when used clinically, will surely give positive results along with assisting medical professionals. Our model also predicts the probability (how sure it is) of its prediction, which will come a long way in ensuring a better decision making process.