Leukemia Classification using a Convolutional Neural Network of AML Images

Abstract

Among the most pressing issues in the field of illness diagnostics is identifying and diagnosing leukemia at its earliest stages, which requires accurate distinction of malignant leukocytes at a low cost. Leukemia is quite common, yet laboratory diagnostic centres often lack the necessary technology to diagnose the disease properly, and the available procedures take a long time. They are considering the efficacy of machine learning (ML) in illness diagnostics and that deep learning as a machine learning method is becoming critical. This study proposes a convolutional neural network (CNN) deep learning model for leukemia diagnosis utilizing the AML (acute myeloid leukemia) dataset. The classification using the proposed method achieved results that exceeded 98% accuracy, the sensitivity of 94.73% and specificity of 98.87%.

Image Fusion Algorithm using Grey Wolf optimization with Shuffled Frog Leaping Algorithm

“…It combines two optimization algorithms together which are Shuffled complex evolution algorithm (SCE) and Particle swarm optimization (PSO). In the next sections, we will discuss the basic principles of both algorithms [14], [15], [16], [17], [18], [19].…”

Diabetic Retinopathy Image Classification Using Shift Window Transformer

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