Abstract
Background Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented techniques can be used to help physicians identify and classify ALL rapidly. Materials and Method. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet-50) and VGG-16 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2∗2 max-pooling layers—with strides 2—was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. Results The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet-50, VGG-16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). Conclusion This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.
Highlights
Leukemia is a type of cancer that affects the bone marrow and is divided into four main categories: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphoid leukemia (CLL), and chronic myeloid leukemia (CML) [1, 2]
Due to the multiple challenges in diagnosing acute lymphoblastic leukemia, pathological examination of blood slides by intelligent algorithms can help physicians definitively diagnose, and pathologists can use these methods to analyze microscopic images and treatment measures. erefore, this study aims to provide accurate and immediate diagnosis and classification of acute lymphoblastic leukemia by processing patients’ microscopic images. is diagnostic approach will be based on deep learning and machine learning techniques
By dividing data into training and test data, one accuracy for the training data and one for the test data are achieved during each cycle. e mean accuracy of training and test networks for 100 training cycles to classify into two classes of healthy and leukemia is calculated for the final evaluation of the network as validation accuracy
Summary
Leukemia is a type of cancer that affects the bone marrow and is divided into four main categories: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphoid leukemia (CLL), and chronic myeloid leukemia (CML) [1, 2]. Acute lymphoblastic leukemia is a type of cancer that affects the lymphocytes and lymphocyte-producing cells in the bone marrow and is the second most common acute leukemia in adults [3]. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Diagnosis of this disease can have a significant impact on the treatment of this disease. A convolutional network with ten convolutional layers and 2 ∗ 2 max-pooling layers—with strides 2—was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. Is study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. is proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis
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