Abstract: A content-based image retrieval (CBIR) system that is applied to blood smear images diagnoses whether sickle cell disease is present. Deep Learning (DL) based Convolutional Neural Networks (CNNs) are designed to recognize visual patterns directly from image pixels with minimal preprocessing. Using pre-trained CNNs as a feature extractor provides an alternative to the handcrafted features that are not manually engineered from raw pixel data in general machine learning (ML) classifiers. The invention relies on extracting features from blood smear images by applying Deep Transfer Learning with three pre-trained models: ResNet-50, Inception V3, and VGG16 using Python with OpenCV and Keras libraries.

Abstract: Disclosed is a content-based image retrieval (CBIR) system and related methods that serve as a diagnostic aid for diagnosing whether a dermoscopic image correlates to a skin cancer type. Systems and methods according to aspects of the invention use as a reference a set of images of pathologically confirmed benign or malignant past cases from a collection of different classes that are of high similarity to the unknown new case in question, along with their diagnostic profiles. Systems and methods according to aspects of the invention predict what class of skin cancer is associated with a particular patient skin lesion, and may be employed as a diagnostic aid for general practitioners and dermatologists.

Abstract: Disclosed is a content-based image retrieval (CBIR) system and related methods that serve as a diagnostic aid for diagnosing whether a dermoscopic image correlates to a skin cancer type. Systems and methods according to aspects of the invention use as a reference a set of images of pathologically confirmed benign or malignant past cases from a collection of different classes that are of high similarity to the unknown new case in question, along with their diagnostic profiles. Systems and methods according to aspects of the invention predict what class of skin cancer is associated with a particular patient skin lesion, and may be employed as a diagnostic aid for general practitioners and dermatologists.