Developing a Lightweight CNN-Based Model for Enhanced Automatic White Blood Cell Classification Using Deep Learning Techniques

Abstract

Finding and monitoring many diseases, including infections and leukaemia, depend on the classification of white blood cells (WBCs). Conventional techniques mostly rely on seeing samples under a microscope and calling for an expert to evaluate the outcomes. This method takes a lot of time and human mistake might create errors. This work uses a lightweight convolutional neural network (CNN) to provide a basic model to enhance the automated categorisation of white blood cells (WBCs) via deep learning. The proposed paradigm maximises computational efficiency, hence it is perfect for real-time applications and environments with limited resources. We kept the model correct while still designing a simplified form that would make using it easier. Trained and validated on a whole dataset including diverse microscopy images of WBCs, the model guarantees stability and generalizability throughout many imaging environments. Our findings reveal that, in terms of sorting accuracy, speed of processing, and operational efficiency, we have achieved significant progress over conventional techniques. The minimal weight of the device makes it simple to use in internet services and mobile health applications, therefore enabling more individuals to get necessary medical attention. By means of concepts on building efficient deep learning models for healthcare, this work provides a beneficial tool for better categorising white blood cells and also helps enhance medical photographs.