Chest CT Segmentation Dataset

This hair loss dataset provides alopecia images of women captured from the top and front sides, labeled into three classes based on the Ludwig scale, with metadata (age, gender, ethnicity) to support machine learning, deep learning, and AI models for hair loss detection, early diagnosis, and research on hair thinning and scalp health.

Men Hair Loss Segmentation Dataset is an accurately labeled bald men dataset consisting of multi-angle images of individuals with varying degrees of hair loss, labeled for alopecia classification, and designed to support machine learning models in analyzing scalp conditions, hair thinning, and developing hair restoration and hair transplantation solutions using advanced deep learning techniques.

This hair loss dataset features annotated images of women with varying degrees of hair loss, designed for machine learning and deep learning applications focused on analyzing hair density, scalp conditions, and developing solutions for hair restoration, hair thinning, and alopecia treatment

The hair loss dataset contains high-resolution scalp images of people captured from five sides, labeled with seven classes on the Norwood-Hamilton scale and supplemented with hair follicle annotations to help machine learning models analyze hair density, thinning patterns, and diagnose baldness

Dataset of volumetric OCT scans of retinal layers with annotations for structural segmentation and disease diagnosis.

The dataset of digital X-ray includes over 1,200,000+ studies of the chest, shoulder, elbow, and wrist joints.

Dataset of  x-ray of the mammary includes over 600,000 studies with protocols developed by medical professionals

Dataset of CT scans of the brain includes over 70,000+ studies with protocols developed by medical professionals

It is a large-scale CT scan chest dataset featuring over 150,000 chest CT images with annotated pathologies, designed for training deep learning models in lung disease detection, cancer diagnosis, and medical imaging tasks, with labeled data covering a wide range of conditions such as pulmonary embolism, tuberculosis, and lung cancer.

It is a detailed brain CT dataset featuring over 1,000 annotated CT scans for tumor segmentation, brain hemorrhage detection, and other pathology classification tasks, designed to train machine learning models in semantic segmentation, treatment planning, and accurate analysis of brain tissues, structures, and injuries in medical imaging.