This chapter presents deep learning methodologies for medical imaging tasks. The chapter starts with echocardiography for early detection of myocardial infarction (MI) or commonly known as heart attack. Early and fundamental signs of MI can be visible as the abnormality in one or several segments of the left ventricle (LV) wall, where a segment may move “abnormally” or “nonuniformly.” The primary tool to detect and identify such regional wall motion abnormalities is echocardiography, which is a fast, cost-effective, and lowest risk imaging option. A three-phase approach is introduced, where the entire LV wall is segmented by a deep learning model, and then characteristics of the segmented wall are used to perform early detection of MI robustly and accurately. The second medical imaging task discussed in the chapter is the recognition of coronavirus disease 2019 (COVID-19), which has become a global health concern after it is declared as a pandemic in March 2020. Developing automatic, accurate, and fast algorithms for COVID-19 detection plays a vital role in the prevention of spreading the virus. Deep learning models can provide state-of-the-art performance in many imaging tasks. However, due to data scarcity, these models cannot produce satisfactory results when trained for COVID-19 recognition. To tackle this issue, Convolutional Support Estimator Network (CSEN) is introduced due to its advantage over a scarce-data classification task for robust COVID-19 recognition using chest X-ray images. In order to utilize the CSEN classification scheme, features are extracted from a state-of-the-art deep neural network. Consequently, the introduced network can achieve an elegant performance for COVID-19 recognition.