For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

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IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning.

A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia

A survey on applications of deep learning in microscopy image analysis.

Introduction. The early detection and diagnosis of leukemia, i.e., the precise differentiation of malignant leukocytes with minimum costs in the early stages of the disease, is a major problem in the domain of disease diagnosis. Despite the high prevalence of leukemia, there is a shortage of flow cytometry equipment, and the methods available at laboratory diagnostic centers are time-consuming. Motivated by the capabilities of machine learning (machine learning (ML)) in disease diagnosis, the present systematic review was conducted to review the studies aiming to discover and classify leukemia by using machine learning. Methods. A systematic search in four databases (PubMed, Scopus, Web of Science, and ScienceDirect) and Google Scholar was performed via a search strategy using Machine Learning (ML), leukemia, peripheral blood smear (PBS) image, detection, diagnosis, and classification as the keywords. Initially, 116 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles remained as the population of the study. Results. This review study presents a comprehensive and systematic view of the status of all published ML-based leukemia detection and classification models that process PBS images. The average accuracy of the ML methods applied in PBS image analysis to detect leukemia was >97%, indicating that the use of ML could lead to extraordinary outcomes in leukemia detection from PBS images. Among all ML techniques, deep learning (DL) achieved higher precision and sensitivity in detecting different cases of leukemia, compared to its precedents. ML has many applications in analyzing different types of leukemia images, but the use of ML algorithms to detect acute lymphoblastic leukemia (ALL) has attracted the greatest attention in the fields of hematology and artificial intelligence. Conclusion. Using the ML method to process leukemia smear images can improve accuracy, reduce diagnosis time, and provide faster, cheaper, and safer diagnostic services. In addition to the current diagnostic methods, clinical and laboratory experts can also adopt ML methods in laboratory applications and tools.

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Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Automatic Leukemia or blood cancer detection is a challenging job and is very much required in healthcare centers. It has a significant role in early diagnosis and treatment planning. Leukemia is a hematological disorder that starts from the bone marrow and affects white blood cells (WBCs). Microscopic analysis of WBCs is a preferred approach for an early detection of Leukemia since it is cost-effective and less painful. Very few literature reviews have been done to demonstrate a comprehensive analysis of deep and machine learning-based Acute Lymphoblastic Leukemia (ALL) detection. This article presents a systematic review of the recent advancements in this knowledge domain. Here, various artificial intelligence-based ALL detection approaches are analyzed in a systematic manner with merits and demits. The review of these schemes is conducted in a structured manner. For this purpose, segmentation schemes are broadly categorized into signal and image processing-based techniques, conventional machine learning-based techniques, and deep learning-based techniques. Conventional machine learning-based ALL classification approaches are categorized into supervised and unsupervised machine learning is presented. In addition, deep learning-based classification methods are categorized into Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and the Autoencoder. Then, CNN-based classification schemes are further categorized into conventional CNN, transfer learning, and other advancements in CNN. A brief discussion of these schemes and their importance in ALL classification are also presented. Moreover, a critical analysis is performed to present a clear idea about the recent research in this field. Finally, various challenging issues and future scopes are discussed that may assist readers in formulating new research problems in this domain.

A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia

Mutual Information based hybrid model and deep learning for Acute Lymphocytic Leukemia detection in single cell blood smear images.

The counting of Red Blood Cells (RBC), White Blood Cells (WBC) and platelet of human blood in microscopic image can provide useful information concerning the health of the patient. The conventional methods used in laboratories to detect and diagnose the problem were very slow and prone to get inaccuracy in result. Digital image processing can detect and recognize different diseases and make it easy to analyze and diagnose the actual problem in a fast and cost effective manner. The major intension of this paper is to count number of WBC and also identify the abnormal ones. The proposed work is useful to detect different kind of diseases like Chronic Obstructive Pulmonary Disease, Immune system disorders, Neutropenia, HIV/AIDS, Lymphocytopenia, leukemia etc on the basis of counts of different blood constituents. This paper is going to provide way to recognize types of different kind of WBC in its normal and abnormal form. This will also helpful for hematologists for clear identification and counting of blood cells. Finally the proposed work designed to obtain effective and more accurate result. The result indicates 85% accuracy for identification and counting of different type of blood cells.

Detection of abnormal blood cells on the basis of nucleus shape and counting of WBC

Acute lymphoblastic leukaemia (ALL), which is due to the malfunctioning in the bone marrow, is common among people all over the world. The haematologist suffers a lot to discriminate the presence of leukaemia in the patients using the blood smears. To overcome the inaccuracy and reliability issues, this paper proposes an automatic method of leukaemia detection, named chronological Sine Cosine Algorithm–based actor-critic neural network (Chrono-SCA-ACNN). Initially, the blood smear images are segmented using the proposed entropy-based hybrid model, from which the image-level features and statistical features are extracted from the segments. Then, the selected features are applied to the proposed classifier, which detects the leukaemia. In the proposed Chrono-SCA-ACNN, the optimal weights are selected by the proposed Chrono-SCA, which is the integration of the chronological concept in the SCA. Finally, the experimentation is performed using the ALL-IDB2 database, and the effectiveness of the proposed method over the existing methods is evaluated. From the analysis, the accuracy of the proposed method is found to be 0.99, which proves that it outperforms the existing classification methodologies.

Nucleus and cytoplasm-based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images.

The analysis of blood cells in microscope image can provide useful information concerning the health of the patient. To analyze, identify and diagnose - digital image processing techniques is much more effective than manual observation. The major intension of this paper is to detect the nucleus and cytoplasm of blood cells. The proposed work is useful to detect different kind of disease like anemia, leukemia etc on the basis of the condition of nucleus. This will also helpful for hematologists for clear identification and counting of blood cells. Finally the proposed work designed to obtain effective and more accurate result than other conventional edge detection techniques like Canny, Sobel, and Laplacian of a Gaussian. The result indicates 85% accuracy for identification of different type of cells as well as nucleus.

A New Approach for Segmentation and Identification of Disease Affected Blood Cells

Background and Objective: Leukemia identification, detection, & classification has erupted an intriguing field in medical research. Several methodologies are convenient in theprevious work to detect five types WBCs (lymphocytes, eosinophils, monocytes, neutrophils, and basophils). Single cell Blood's smear images used for experiment. Propounded method is used for leukemia recognition, uncovering and distribution based on FAB classification. Methodology: This propounded task has developed French-American and British (FAB) classification-based detection module on blood smearimages (BSIs). Methods like pretreatment, segmentation, feature extraction, distribution are used in detection method. The Propounded algorithm-based propounded model is used for segmentation, which is combination of the segmented results of the Linde-Buzo-Gray (LBG) algorithm, Adaptive canny used for edge identification and Hysteresis and watershed algorithm used for thresholding. The shape, texture features, color of segmented image are picked by neural network and classification is performed by Support Vector Machine (SVM) and prediction by Naive Bayes Classifier (NBC). Result: Dataset-master and Cellavison dataset is being used for the experimentation. The BSIs are considered for the Evaluation based on ROC curve analysis metrics like TPR, TNR and accuracy. Our propounded solution obtains superior classification performance in the given dataset. The suggested classifier enhanced the classification average accuracy to 99.06% and Mean Square Error (MSE) is 0.0407. Conclusion: The enhanced accuracy had achieved by enhancing performance and classification with comparison with some other methods.

Krishna Kumar Jha

Papers

Mutual Information based hybrid model and deep learning for Acute Lymphocytic Leukemia detection in single cell blood smear images.

Detection of abnormal blood cells on the basis of nucleus shape and counting of WBC

Nucleus and cytoplasm-based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images.

A New Approach for Segmentation and Identification of Disease Affected Blood Cells

FAB Classification based Leukemia Identification and prediction using Machine Learning