Performance Analysis for Optimized Light Weight CNN Model for Leukemia Detection and Classification using Microscopic Blood Smear Images

Large number of diseases can be diagnosed by the counting and classification of blood cells. One type of the most common blood diseases is Acute Lymphoblastic Leukemia (ALL). Rapid and uncontrolled growth of immature leukemic cells (also named as blast cells) is used to characterize ALL. The morphological analysis of the bone marrow and blood smear is the primary step of diagnosis. Valuable information is provided by the features extracted from the blood cells for the confirmation of the diagnosis. As the process have numerous drawbacks, a computerized system will be helpful for the analysis of stained microscopic images of the blood cells for quantitative examination. This paper demonstrates a new and simple segmentation approach using watershed transform and two simpler classification models to diagnose the presence of ALL in blood smear accurately.

Leukemia is a very dangerous kind of malignancy troubling the blood or bone marrow in all age categories, both in adults and children. The deadly and threatening kind of leukemia is named Acute Lymphoblastic Leukemia (ALL). The accurate and automated ALL diagnosis of blood cancer is complex work. Medical experts and hematologists in the bone marrow and blood samples detect it by employing a high-quality microscope. The manual classification is observed as tiresome and is restricted by varying expert considerations and other attributes. Presently, the Convolutional Neural Networks (CNNs) have become an acceptable mechanism for analyzing the medical image. However, for attaining outstanding performance, conventional CNNs normally demand large data sources for better training. Thus, to alleviate the existing complexities, we implemented an effective ALL detection system using deep learning. At first, the necessitated images are aggregated from global resources of data. Further, the garnered images are inputted into the Optimized Trans-Res-Unet+ (OTRUnet+)-based segmentation model. Here, the Fitness-aided Position Updating in the Social engineering Algorithm (FPUSA) for improving the segmentation process’s efficacy optimally tunes the OTRUnet+ technique parameters. In addition, the segmented images are taken to perform the classification process using the Adaptive Multi-Dilated Residual Attention Network (AMDRAN); here several parameters are optimally tuned by the same FPUSA to enrich the classification process. Finally, the suggested AMDRAN technique offered the ALL classified output. The effectiveness of the designed ALL detection system is explored with several existing systems to display its enhanced performance over other models

Glucocorticoids, particularly prednisone/ prednisolone and dexamethasone, play a prominent role in the treatment of pediatric patients with acute lymphoblastic leukemia due to their ability to induce apoptosis in susceptible cells. Current therapeutic protocols use prednisone for both the prophase and the induction phase of the therapy because the greater antileukemic activity of dexamethasone is compromised by its high frequency of serious adverse reactions. To compare, for the first time, the in vitro antileukemic activity of prednisolone alone to that of a combination of prednisolone and dexamethasone using dexamethasone at a very low and presumably safe dosage (1/50 w/w). Lymphoblasts were isolated from bone marrow and/or blood samples from children with newly diagnosed acute lymphoblastic leukemia. The cytotoxic activity of prednisolone, dexamethasone and the prednisolone/dexamethasone combination against isolated leukemia cells was analyzed using the MTT cytotoxicity assay. We observed differences in the in vitro antileukemic activity of prednisolone and dexamethasone in 21% of the tested patients. 3% of the children were prednisolone sensitive but dexamethasone resistant, while 18% were prednisolone resistant and dexamethasone sensitive. 32% were sensitive to both glucocorticoids and 18% were resistant to both. Cells from patients with good in vivo responses to prednisone monotherapy were more responsive to prednisolone in vitro than were cells from patients with poor prednisone responses (P<0.07). Importantly, we demonstrated that the use of even a minimal dose (1/50 w/w) of dexamethasone with prednisolone dramatically increases the in vitro anti-leukemic activity of prednisolone (P<0.0006). The high inter-individual variability of acute lymphoblastic leukemia responses to glucocorticoids suggest that either patients should be selected for prednisone or dexamethasone treatment on the basis of predictive biomarkers or that prednisone should be used directly in combination with a very low and safe dose of dexamethasone to potentiate its antileukemic activity. The latter option is likely to be cheaper and more efficient, and therefore warrants further clinical investigation to assess its efficacy and safety in treating childhood acute lymphoblastic leukemia.

Acute Lymphoblastic Leukemia (ALL) is the most common pediatric cancer, requiring early detection to save lives and reduce the financial burden of advanced-stage treatment. While traditional diagnostic methods are time-consuming and resource-intensive, deep transfer learning offers a computationally efficient alternative for medical image classification. This study employed two widely recognized transfer learning algorithms, VGG-19 and EfficientNet-B3, to detect ALL using a publicly available dataset of 10,661 images from 118 patients. Data preprocessing included resizing, augmentation, and normalization. The models were trained for 100 epochs, with batch sizes of 30 for VGG-19 and 32 for EfficientNet-B3. Evaluation metrics such as accuracy, precision, recall, and F1 score were used to assess model performance. Statistical significance testing was performed using paired t-tests (p<0.05). Comparative analysis was performed with existing studies to validate the findings. EfficientNet-B3 significantly outperformed VGG-19, achieving an average accuracy of 96% compared to 80% for VGG-19 (p<0.001). EfficientNet-B3 demonstrated superior performance in handling class imbalance, with the minority class (Hem) achieving precision, recall, and F1 scores of 97%, 89%, and 93%, respectively. VGG-19 struggled with the minority class, achieving lower recall (51%) and F1 score (62%). However, dataset limitations including single-source origin may affect generalizability. This study highlights the effectiveness of EfficientNet-B3 as a reliable tool for early ALL detection, offering high accuracy and computational efficiency. Clinical implementation requires addressing computational constraints and integration challenges. Future research could integrate multimodal datasets to identify risk factors and further improve diagnostic accuracy.

Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children’s bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929.

Hematologic malignancy puts the lives of numerous people of all the ages at serious risk globally. Acute lymphoblastic leukemia (ALL), a sub type of leukemia, is a dreadful disease that affects millions of individual globally. Early detection is crucial, and image analysis of peripheral blood smears is a simple way to diagnose and categorize leukemic cells. However, image analysis is complex and can lead to incorrect findings. To address this, a model is proposed to examine ALL and identify its various categories using deep ensemble learning. This study shows that a deep learning method based on an ensemble weight threshold improves leukemia detection using imbalanced dataset which reflects the real-life scenario. The proposed model examines healthy and ALL cells and identify its various categories, including Benign, Pro-B, Early Pre-B, and Pre-B from peripheral blood smear images to help in the early detection of this life-threatening disease with the highest accuracy (99.96%), precision (99.86%), sensitivity (99.93%), specificity (99.97%), F1-Score (99.89%). The model also outperforms five other pre-trained networks, viz. AlexNet, VGG-16, DenseNet-210, ResNet-50, and MobileNetV2 in classification accuracy, demonstrating its potential as an effective early diagnostic tool for ALL and its subtypes

To contain the spread of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), the Norwegian government implemented lock-down restrictions from 13.03.2020: schools, day cares and after-school activities were closed, 2-week quarantine was required following international travel, physical distancing (2 metres) was recommended and groups were restricted to 5 people.

Background: Immunophenotypic analysis of leukemic cells by multicolor flow cytometry using different monoclonal antibodies labeled to various flourochromes, is an important, precise and rapid investigation for diagnosis, classification, prognosis prediction, and minimal residual disease detection in acute lymphoblastic leukemia (ALL). Objectives: The aims of study were to study the expression of CD45, CD34, CD10, human leukocyte antigen-DR (HLA-DR) in B and T-ALL among 114 Iraqi patients and compare findings with other reports worldwide. Patients, Materials and Methods: A retrospective cross-sectional study was conducted on 114 ALL patients of various age groups from different hematology centers in Baghdad who were referred to Flowcytometry Department at the Bone Marrow Transplantation Center/Private Nursing Home Hospital/Medical City for immunophenotypic classification using multi-color flow cytometry from the January 1, 2016, to the August 31, 2016. Results: Out of 114 patients, 71 patients were pediatric patients and 43 were adults. Eighty-three patients were classified as B-cell ALL and 31 patients as T-cell ALL. There was significant association between male gender, high white blood cells count and T-cell ALL subtype. Negative-dim-moderate CD45, positive CD34, and positive HLA-DR expressions were significantly associated with B-cell ALL. Common ALL antigen was seen in 86.7% of the B-cell ALL patients and was accompanied cTdT expression. Aberrant myeloid antigens were observed in 22.9% of B-cell ALL patients and in 35.5% of T-cell ALL. About 4.8% of the patients with B-cell ALL had aberrant T-linage antigens while 6.5% of the patients with T-cell ALL expressed aberrant B-cell lineage antigens. Conclusion: Immunophenotypic expression of ALL cells among Iraqi patients is to somewhat in accordance with various researches worldwide making immunophenotyping flow cytometry a crucial appliance in diagnosis, classification, risk stratification, and minimal residual disease detection in ALL.

CD22 Expression in Pediatric B-Lineage Acute Lymphoblastic Leukemia.

Automatic identification of blast leukemic cells types using deep learning with vision transformers.

Minimal Residual Disease following Allogeneic Hematopoietic Stem Cell Transplantation

Prognostic Significance of Cytogenetic Abnormalities in Patients with Philadelphia-Negative ALL Undergoing Allogeneic Hematopoietic Stem Cell Transplantation in Complete Remission: A CIBMTR Analysis

This article presents an enhanced segmentation methodology for the accurate detection of acute lymphoblastic leukemia (ALL) in blood smear images. The proposed approach integrates color correction techniques with HSV color space segmentation to improve white blood cell analysis. Our method addresses common challenges in microscopic image processing, including sensor nonlinearity, uneven illumination, and color distortions. The key objectives of this study are to develop a robust preprocessing pipeline that normalizes blood smear images for consistent analysis, implement an HSV-based segmentation technique optimized for leukocyte detection, and validate the method's effectiveness across various ALL subtypes using clinical samples. The proposed technique was evaluated using real-world blood smear samples from ALL patients. Quantitative analysis demonstrates significant improvements in segmentation accuracy compared to traditional methods. Our approach shows strong capability in reliably detecting and segmenting ALL subtypes, offering the potential for enhanced diagnostic support in clinical settings.

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists’ expertise, specialized features, and subject knowledge. Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. In this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for the classification of blood cell images, focusing on eosinophils, lymphocytes, monocytes, and neutrophils. To tackle challenges like vanishing gradients and enhance feature extraction, the model incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence. Conventional residual blocks are strategically placed between layers to preserve original information and extract general feature maps. Global and Local Feature Enhancement Blocks (GLFEB) balance weak contributions from shallow layers for improved feature normalization. The global feature from the initial convolution layer, when combined with GLFEB-processed features, reinforces classification representations. The Tanh function introduces non-linearity. A Channel and Spatial Attention Block (CSAB) is integrated into the neural network to emphasize or minimize specific feature channels, while fully connected layers transform the data. The use of a sigmoid activation function concentrates on relevant features for multiclass lymphoblastic leukemia classification The model was analyzed with Kaggle dataset (16,249 images) categorized into four classes, with a training and testing ratio of 80:20. Experimental results showed that DRDB, GLFEB and CSAB blocks’ feature discrimination ability boosted the DDRNet model F1 score to 0.96 with minimal computational complexity and optimum classification accuracy of 99.86% and 91.98% for training and testing data. The DDRNet model stands out from existing methods due to its high testing accuracy of 91.98%, F1 score of 0.96, minimal computational complexity, and enhanced feature discrimination ability. The strategic combination of these blocks (DRDB, GLFEB, and CSAB) are designed to address specific challenges in the classification process, leading to improved discrimination of features crucial for accurate multi-class blood cell image identification. Their effective integration within the model contributes to the superior performance of DDRNet.

Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies.