YOLOV5 : Classify Kaggle WBC data set using transfer learning approach using LISC data set.

Abstract

White blood cells (WBCs), are essential constituents of the immune system, by providing organism's defence against infections, inflammation, and various diseases. Types of WBCs, possessing specific functions and attributes that are vital for the maintenance of health. The categories of WBCs are neutrophils, lymphocytes, monocytes, eosinophils, and basophils, each performing functions in the boost immune response. YOLOv5 stands out as a top-tier object detection model recognized for its speed and accuracy in detecting objects from images. The YOLOv5 model serves as an effective tool for detecting and classifying different white blood cell types in blood smear images during WBC classification and identification tasks. In Transfer learning A model developed for a specific task is reused with change in hyperparameters, as the starting point for a model on a second task. This approach is particularly valuable in scenarios where limited labelled data is available for the target task, allowing practitioners to leverage knowledge gained from related tasks with ample data. Transfer learning is commonly used in deep learning, especially for tasks such as image classification, natural language processing, and speech recognition. Kaggle and LISC original data set has limited number of images and YOLOv5 is state of art model for object detection and classification, and the employing transfer learning is the core idea of this research. Learning features from Original LISC data set and applying best weights produced from previous run we applied that weight on Augmented images of Kaggle data set and produced accuracy of 99.5mAP@50, recall 99 and F1-Score 99.