MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

Abstract

Leukemia, often called blood cancer, is a disease that primarily affects white blood cells (WBCs), which harms a person's tissues and plasma. This condition may be fatal when if it is not diagnosed and recognized at an early stage. The physical technique and lab procedures for Leukaemia identification are considered time-consuming. It is crucial to use a quick and unexpected way to identify different forms of Leukaemia. Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment. Various deep-learning (DL) model-based segmentation and categorization techniques have already been introduced, although they still have certain drawbacks. In order to enhance feature extraction and classification in such a practical way, Mayfly optimization with Generative Adversarial Network (MayGAN) is introduced in this research. Furthermore, Generative Adversarial System (GAS) is integrated with Principal Component Analysis (PCA) in the feature-extracted model to classify the type of blood cancer in the data. The semantic technique and morphological procedures using geometric features are used to segment the cells that makeup Leukaemia. Acute lymphocytic Leukaemia (ALL), acute myelogenous Leukaemia (AML), chronic lymphocytic Leukaemia (CLL), chronic myelogenous Leukaemia (CML), and aberrant White Blood Cancers (WBCs) are all successfully classified by the proposed MayGAN model. The proposed MayGAN identifies the abnormal activity in the WBC, considering the geometric features. Compared with the state-of-the-art methods, the proposed MayGAN achieves 99.8% accuracy, 98.5% precision, 99.7% recall, 97.4% F1-score, and 98.5% Dice similarity coefficient (DSC).

Enhancing Brain Tumor Classification with Transfer Learning across Multiple Classes: An In-Depth Analysis

“…The study successfully measured arm volumes in 730 arms from 365 women, showing strong reliability with a mean absolute error of 36.65 mL, a mean percent error of 1.69%, and a 0.992 correlation with actual volumes. Veeraiah et al [27] introduce MayGAN for Leukemia classification with 99.8% accuracy. Aryai et al [28] present MDML-RP for efficient health monitoring in WBANs, achieving substantial performance gains.…”

A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis

“…Numerous methods were developed to diagnose leukemia using neural networks, such as in [1][2][3][4][5]. All methods, except those in [1], implemented work to detect tumor cells without the capability of classifying these cells into their suitable types. Only the developed method in [1] could classify leukemia into ALL, AML, CLL, and CML.…”

“…All methods, except those in [1], implemented work to detect tumor cells without the capability of classifying these cells into their suitable types. Only the developed method in [1] could classify leukemia into ALL, AML, CLL, and CML. However, this algorithm suffers from a drawback as some of its evaluated metrics did not exceed 98.5%.…”

“…Blood cancer affects children and adults [1]. Blood cancer is also known as leukemia and it occurs due to changes in white blood cells [1,2]. Leukemia refers to the uncontrolled growth of blood cells [3].…”

“…Acute lymphoblastic leukemia (ALL) affects children and older adults (65 years old and over) [1,14,15]. Pathologists and physicians consider acute myeloid leukemia (AML) the most fatal type since just under 30% of patients have survived in the past five years [1,16]. The…”