Abstract: A method of analyzing images and generating a report is disclosed. The method may include inputting a test image to a trained prediction model. The trained prediction model may be a deep learning-based model. The method may further include obtaining at least one abnormality associated with the test image, using the trained prediction model. The method may further include identifying at least one relevant predetermined region from a plurality of predetermined regions associated with the test image, using the trained prediction model, based on the at least one abnormality. Each of the at least one relevant predetermined region is identified based on hierarchical classification. The method may further include determining within each of the at least one relevant predetermined region, a location associated with the at least one abnormality.

Abstract: A method of image processing and classifying target entities with an image is disclosed that may include applying a contrast amplification procedure to a Lightness parameter associated with an input image, to amplify contrast of the input image and obtain an amplified-contrast image. The method may further include de-noising the amplified-contrast image by iteratively performing on the amplified-contrast image a blur correction, an erosion correction, and a dilation correction, to obtain a de-noised image corresponding to the amplified-contrast image. The method may further include determining edges of each of one or more target entities associated with the input image from the de-noised image and identifying the one or more target entities associated with the input image based on the identified edges, to generate a contoured image. The method may further include classifying the one or more target entities into one or more predefined classes, using a classification model.