Multiscale adaptive and attention-dilated convolutional neural network for efficient leukemia detection model with multiscale trans-res-Unet3+-based segmentation network

A precise early-stage leukemia diagnosis is essential for treating patients and saving their lives. The least expensive way for the initial diagnosis of leukemia in patients is the microscopy imaging analysis. However, this work is subjective and time-consuming. Hence, this paper creates an efficient leukemia detection model using deep learning approaches. Initially, the standard leukemia datasets are used to collect the images. The gathered images are given for the region segmentation to the Multiscale Trans-Res-Unet3+ (MTResUnet3+) Network. The segmented regions from the MTResUnet3+ are now considered for the feature extraction phase from which the most relevant attributes are mined. Here, the features like color, shape, and texture are extracted separately for performing efficient detection. Further, the features being extracted are considered for the feature selection phase, where the Election-Based Chameleon Swarm Algorithm (E-CSA) is utilized to optimally select the most appropriate features with the aim of enhancing the performance rate of the developed model. The optimally selected features are given to the next stage for detecting the presence of leukemia. Here, the Multiscale Adaptive and Attention-based Dilated Convolutional Neural Network (MAA-DCNN) is made for detecting leukemia, in which the optimization of the parameter is done with the help of hybrid E-CSA in order to elevate the detection accuracy of leukemia. The simulation analysis is performed to analyze the performance rate of the recommended leukemia detection model by contrasting it with the conventional leukemia detection models and existing algorithms using various performance metrics for validation.