Machine learning approach of automatic identification and counting of blood cells (RBC, WBC, and Platelet) with KNN and IOU based verification.

The complete blood count (CBC) dataset contains a total of 360 blood smear images of red blood cells (RBCs), white blood cells (WBCs), and Platelets with annotations.

Large-Scale Multi-Class Image-Based Cell Classification with Deep Learning

The purpose of our project is to develop a system that can automatically detect cancer from the blood cell images. This system uses a convolution network that inputs a blood cell images and outputs whether the cell is infected with cancer or not.

Using deep learning and CNN model

Blood cells image segmentation and counting using deep transfer learning.

This is a complete service of blood cell image classification using a Convolutional Neural Network, with an FastAPI backend and ReactJS web app

Using TensorFlow Object Detection API to detect blood cells

MicrOscopic VisuAlization of BLood cElls for the Detection of Malaria and CD4+

My Blood Cell Report is a native iOS mobile application developed using a Machine learning text classification model trained in coreML (apple developer framework) to measure the impact of diseases on blood cells using symptoms as input (provided in texts in the application).

Competition in Artificial Neural Networks and Deep Learning course (2024-2025). The task is to develop a multi-class classification model to classify 96x96 RGB images of blood cells into eight classes, each representing a distinct cell state.

Working demo can be found at

Detecting Blood Cells in Blood Images using a patch-based Neural Network approach