Color-Based Hybrid Modeling to Classify the Acute Lymphoblastic Leukemia

Many researchers have classified acute lymphoblastic leukemia using several methods. One of the methods is a convolutional neural network. However, the limitation of the convolutional neural network is a large number of trainable parameters updated. The paper proposes a new architecture based on the convolutional neural network. We have designed and implemented a convolutional neural network with different kernels, where we increase the number of kernels like pyramid models. We utilized the final convolution to conduct the fully connected, followed by the SoftMax function to classify the image. We have evaluated our proposed architecture using acute lymphoblastic leukemia image database (ALL-IDB2). The results show that our proposed method produced the average Accuracy, Precision, and Recall of 99.17%, 99.33%, and 99%, respectively. It has outperformed other models, i.e., shape features, Multi distance of GLCM, CNN and SVM, Pretrained deep convolutional neural networks, AlexNet, ensemble network, convolutional and recurrent neural network, and hypercomplex-valued convolutional neural networks.

Section: Comparing To Other Methodsmentioning

confidence: 99%

“…Similarly, the color-based segmentation method has been proposed to classify acute lymphoblastic leukemia using main stages, pre-processing, segmentation, feature extraction, and classification [3]. They have employed the shape and texture feature to represent the object of the image.…”

Section: Introductionmentioning

confidence: 99%

A Pyramid Model of Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

2022

“…The deficiency of that method is that there is no feature selection process, object features are only based on object texture, while the shape of the object is not considered as feature, similarity measurements only consider the distance between pixels in the image. Classification of ALL using the color-based hybrid modeling has been proposed [30]. The method has four main steps, not much different from the previous method, these include the preprocessing using the Otsu thresholding-based contrast stretching, the image segmentation using binary image transformation with Otsu algorithm and the biggest object area selection, the features extraction by combining the color-based densitometry and shape features, and then the similarity measurement by the Euclidian distance and Manhattan method.…”

Section: Conventional Methodsmentioning

confidence: 99%

Classification of Acute Lymphoblastic Leukemia on White Blood Cell Microscopy Images Based on Instance Segmentation Using Mask R-CNN

2022

Manual classification of acute lymphoblastic leukemia carried out by doctors will certainly take a lot of time and effort. The challenges in automated computer-based systems for classification of acute lymphoblastic leukemia are when providing proper lightning in stained white blood cell microscopy images and when segmenting the touching or overlapping cells in the image. Previous studies related to the classification of acute lymphoblastic leukemia still require many steps when using conventional methods, whereas when using deep learning methods are still limited to classification without providing analysis for the instance segmentation of lymphoblast in the image. Therefore, we propose instance segmentation using Mask R-CNN on white blood cell microscopy images to classify acute lymphoblastic leukemia that can support the diagnosis process efficiently and effectively. In this study, we implemented Mask R-CNN by transfer learning method to fit the instance segmentation task on white blood cell microscopy images. We added a contrast enhancement process to the image dataset to overcome the bad lightning problem in stained white blood cell microscopy images. We used the real dataset obtained from hospital to evaluate our method. The method we used was able to get 83.72 % accuracy, 85.17 % precision, and 81.61 % sensitivity.

“…A color-based segmentation approach has been suggested for classifying acute lymphoblastic Leukemia based on stages, pre-processing, segmentation, feature extraction, and classification. A 95.38% precision has been attained in representing objects via shape and texture characteristics [5]. Flaws in image segmentation may result in incorrect categorization, necessitating enhancement.…”

Section: Introductionmentioning

confidence: 99%

A New Model: Commutative Hypercomplex – Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

2023

The purpose of the paper is to classify the Leukemia images. In this paper, we proposed a commutative model of a convolutional neural network for Leukemia image classification. We employ commutative hypercomplex modeling A[+1, -1] and A[-1, +1] to build the new model. We hire an augmentation model to enrich the image data sets for the training sets through rotation, zooming, and flipping. We evaluated our proposed method using acute lymphoblastic leukemia image database type 2 (ALL-IDB2). The results show that our proposed method has delivered the best average accuracy at 96.43% for A[+1,-1] and 97.05 for A[-1,+1]. We have measured and found maximum accuracy at 100% for A[+1,-1] and A[-1,+1]. Comparison results show that our proposed method outperformed Knearest neighbor, support vector machine-radial basis function, support vector machine-linear, support vector machinepolynomial, naïve bayes gaussian, naïve bayes complement, decision tree, and colour hybrid modelling.