Ultrasound Fetus Dataset

This dataset contains fetal ultrasound images collected and organized for research on automated medical image classification using deep learning techniques. The dataset is divided into three main subsets: training, testing, and validation, which are used to develop, evaluate, and validate machine learning models. Each subset contains three categorized subfolders representing different diagnostic classes: normal, benign, and malignant conditions identified in fetal ultrasound scans. The normal class includes images representing healthy fetal development without abnormalities. The benign class contains ultrasound images showing non-cancerous or mild abnormal tissue formations, while the malignant class includes images with severe or potentially cancerous abnormalities. The dataset structure facilitates supervised learning experiments by enabling proper model training, hyperparameter tuning, and performance evaluation. These images can be used for research in medical image analysis, computer-aided diagnosis, and deep learning-based fetal health assessment. The dataset aims to support researchers in developing accurate classification models for early detection and clinical decision support in prenatal healthcare.