\u003ci\u003eDeep Learning Models for Medical Imaging\u003c/i\u003e explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow âwithâ and âwithoutâ transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists.\u003cul\u003e \u003cli\u003eProvides a step-by-step approach to develop deep learning models \u003c/li\u003e \u003cli\u003ePresents case studies showing end-to-end implementation (source codes: available upon request)\u003c/li\u003e\u003c/ul\u003e
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