BrandonK007/Yolov5s_Malaria_Detection

This project implements a deep learning pipeline using YOLOv5 for the real-time detection of malaria parasites from blood smear images. The model was trained and tuned on a curated microscopy dataset to assist in early, automated screening for malaria in low-resource clinical settings. ## Malaria Parasite Detection with YOLOv5 This project presents a deep learning pipeline using **YOLOv5** for the **real-time detection of malaria parasites** from blood smear images. The model was trained and tuned on a curated microscopy dataset to assist in early, automated screening for malaria in low-resource clinical settings. By combining **computer vision**, **embedded AI**, and **model optimization**, this work demonstrates how deep learning can contribute to accessible healthcare diagnostics. ## Overview The repository includes the full workflow — from dataset preparation and model training to evaluation, deployment, and performance benchmarking on embedded devices. The model achieves: - **mAP@0.5:** 0.98 - **mAP@[0.5:0.95]:** 0.70 - **Model size reduction:** 45% - **GPU memory reduction:** 30% - **Inference speed:** 5 FPS on Raspberry Pi 5 (768×768 input) Performance remains consistent across variations in staining and microscope conditions. --- **All collected datasets were sourced from NIH.com and were manually bounded using LabelStudio**