Infection level identification for leukemia detection using optimized Support Vector Neural Network

An improved multiclass classification of acute lymphocytic leukemia using enhanced glowworm swarm optimization

Acute Lymphoblastic Leukemia (ALL), a kind of blood cancer, more frequently observed in the pediatric population, causes rapid production of immature White Blood Cells. Most of the diagnostic techniques like bone marrow aspiration, imaging techniques, etc. are time consuming, error-prone, costly and depend on the skill set of experts. The ultimate goal of this work is to develop a computer aided automatic classification system to classify Benign, Early, Pro-B and Pre-B classes of ALL. Images from the publicly available dataset were subjected to pre-processing and Region of Interest is obtained by adapting the proposed Multilevel Hierarchical Marker-Based Watershed Algorithm (MHMW). A subset of most vital features were selected by utilizing nature inspired metaheuristic Enhanced Glowworm Swarm Optimization (EGSO) algorithm. Popular classifiers -Decision tree, Random Forest, Multi-Layer Perceptron, Naive Bayes and Linear, Polynomial, Radial basis function, sigmoid kernels of Support Vector Machine were used for multiclass classification. Performance of the proposed system has been compared with three other popular optimization algorithms- Particle Swarm Optimization, Artificial Bee Colony Optimization and Elephant Herd Optimization. Random Forest fed with the optimized features obtained from the proposed integration of MHMW and EGSO algorithms outperformed other classifiers with 98.23%, 98.25%, 98.23% of accuracy, precision and F1 score respectively.

Towards Diagnostic Intelligent Systems in Leukemia Detection and Classification: A Systematic Review and Meta-analysis

OBJECTIVE

Leukemia is a type of blood cancer that begins in the bone marrow and results in high numbers of abnormal white blood cells. Automated detection and classification of leukemia and its subtypes using artificial intelligence (AI) and machine learning (ML) algorithms plays a significant role in the early diagnosis and treatment of this fatal disease. This study aimed to review and synthesize research findings on AI-based approaches in leukemia detection and classification from peripheral blood smear images.

METHODS

A systematic literature search was conducted across four e-databases (Web of Science, PubMed, Scopus, and IEEE Xplore) from January 2015 to March 2023 by searching the keywords "Leukemia," "Machine Learning," and "Blood Smear Image," as well as their synonyms. All original journal articles and conference papers that used ML algorithms in detecting and classifying leukemia were included. The study quality was assessed using the Qiao Quality Assessment tool.

RESULTS

From 1325 articles identified through a systematic search, 190 studies were eligible for this review. The mean validation accuracy (ACC) of the ML methods applied in the reviewed studies was 95.38%. Among different ML methods, modern techniques were mostly considered to detect and classify leukemia (60.53% of studies). Supervised learning was the dominant ML paradigm (79% of studies). Studies utilized common ML methodologies for leukemia detection and classification, including preprocessing, feature extraction, feature selection, and classification. Deep learning (DL) techniques, especially convolutional neural networks, were the most widely used modern algorithms in the mentioned methodologies. Most studies relied on internal validation (87%). Moreover, K-fold cross-validation and train/test split were the commonly employed validation strategies.

CONCLUSION

AI-based algorithms are widely used in detecting and classifying leukemia with remarkable performance. Future studies should prioritize rigorous external validation to evaluate generalizability.

A robust classification of acute lymphocytic leukemia-based microscopic images with supervised Hilbert-Huang transform

Acute lymphocytic leukemia (ALL) is a malignant condition characterized by the development of blast cells in the bone marrow and their quick dissemination into the bloodstream. It primarily affects children and individuals over the age of 60. Manual blood testing, which has been around for a long time, may be slow. The likelihood of recognizing ALL in its early stages was increased by automating the diagnosis. This research developed an improved criterion for classifying ALL microscopic images into two categories: normal images and blast images. First, to save processing time, innovative image preprocessing techniques were employed to gather data for data augmentation, enhancement, and conversion. The K-means clustering technique was also utilized to effectively segment the relevant nuclei from the background. Furthermore, the most salient features were extracted using an empirical mode decomposition (EMD) based on the Hilbert-Huang transform. MATLAB functions such as principal component analysis, gray level co-occurrence matrix, local binary pattern, shape features, discrete cosine transform, discrete Fourier transform, discrete wavelet transform, and independent component analysis have been used and compared with EMD. The Bayesian regularization (BR) method has been implemented in the neural networks (NNs) classifier. Along with NNs, other classifiers such as support vector machine, K-nearest neighbors, random forest, naive Bayes, logistic regression, and decision tree have been used, evaluated, and contrasted with NNs. According to experimental findings, the ALL-IDB2 (Image Database 2) dataset's NNs-based-EMD model classified objects with an accuracy of 98.7%, sensitivity of 99.3%, and specificity of 98.1%. RESEARCH HIGHLIGHTS: Implement a robust method for classifying normal and blast ALL images in the state of the art using the combination of the BR algorithm and the neural networks classifier. Perform robust data processing via data augmentation and conversion from RGB (Red, Green, and Blue) image LAB (Luminosity, A: color space, B: color space) image. Extract the nuclei correctly from the background image using k-means clustering. Extract the most salient features from the segmented images using EMD in the state of the art of HHT.

A hybrid few-shot multiple-instance learning model predicting the aggressiveness of lymphoma in PET/CT images

BACKGROUND AND OBJECTIVE

Patients with aggressive non-Hodgkin lymphoma (NHL) undergo distinct therapy strategies compared with indolent NHL patients. However, it is challenging to estimate NHL aggressiveness based on visual inspection of positron emission tomography (PET) or computed tomography (CT) images. Since diffuse large B-cell lymphoma (DLBCL) and Follicular lymphoma (FL) are the most typical and dominant aggressive and indolent NHL, respectively, this study aims to develop an artificial-intelligence-enabled model to distinguish DLBCL from FL in PET/CT images as the first step to tackle this challenge.

METHODS

We propose a hybrid few-shot multiple-instance learning model to predict the aggressiveness of the NHL. First, rotation-based self-supervision learning (SSL) has been employed to train the encoder on a large-scale, publicly available CT image dataset. Second, hybrid instance-level features are obtained for each NHL lesion by combining deep features with the radiomics features from both PET and CT modalities. Third, instance-level features are transformed into bag-level (or patient-level) representations. Finally, bag-level representations are fed into a distance-based classifier through few-shot learning to predict NHL aggressiveness.

RESULTS

Our model achieves an accuracy of 0.751 ± 0.008, a sensitivity of 0.787 ± 0.012, a specificity of 0.715 ± 0.013, an F1-score of 0.753 ± 0.009, and an area under the curve (AUC) of 0.795 ± 0.009 at the bag level. It outperforms the typical counterparts that use the radiomic features, random forest for feature selection, and support vector machines (SVMs) as classifiers. The three counterparts yield accuracies of 0.714 ± 0.023, 0.705 ± 0.008, and 0.698 ± 0.008, respectively. Moreover, settings of the SSL training dataset (Deep lesion) and task (rotation), hybrid CT and radiomic PET features, the pool-layer strategy of maximum, and distance-based classifier generate the best model.

CONCLUSIONS

A hybrid few-shot multiple-instance learning model can predict lymphoma aggressiveness in PET/CT images and could be a potential tool for determining therapy strategies. Hybrid features and the combination of SSL, few-shot learning, and weakly supervised learning are the two powerful pillars of the model, and these can be expanded to other medical applications with limited samples and incomplete annotations.

Automated white corpuscles nucleus segmentation using deep neural network from microscopic blood smear

The white corpuscles nucleus segmentation from microscopic blood images is major steps to diagnose blood-related diseases. The perfect and speedy segmentation system assists the hematologists to identify the diseases and take appropriate decision for better treatment. Therefore, fully automated white corpuscles nucleus segmentation model using deep convolution neural network, is proposed in the present study. The proposed model uses the combination of ‘binary_cross_entropy’ and ‘adam’ for maintaining learning rate in each network weight. To validate the potential and capability of the above proposed solution, ALL-IDB2 dataset is used. The complete set of images is partitioned into training and testing set and tedious experimentations have been performed. The best performing model is selected and the obtained training and testing accuracy of best performing model is reported as 98.69 % and 99.02 %, respectively. The staging analysis of proposed model is evaluated using sensitivity, specificity, Jaccard index, dice coefficient, accuracy and structure similarity index. The capability of proposed model is compared with performance of the region-based contour and fuzzy-based level-set method for same set of images and concluded that proposed model method is more accurate and effective for clinical purpose.