CausalFormer-HMC: a hybrid memory-driven transformer with causal reasoning and counterfactual explainability for leukemia diagnosis

The definitive diagnosis of acute lymphoblastic leukemia (ALL), as a highly prevalent cancer, requires invasive, expensive, and time-consuming diagnostic tests. ALL diagnosis using peripheral blood smear (PBS) images plays a vital role in the initial screening of cancer from non-cancer cases. The examination of these PBS images by laboratory users is riddled with problems such as diagnostic error because the nonspecific nature of ALL signs and symptoms often leads to misdiagnosis. Herein, a model based on deep convolutional neural networks (CNNs) is proposed to detect ALL from hematogone cases and then determine ALL subtypes. In this paper, we build a publicly available ALL data set, comprised 3562 PBS images from 89 patients suspected of ALL, including 25 healthy individuals with a benign diagnosis (hematogone) and 64 patients with a definitive diagnosis of ALL subtypes. After color thresholding-based segmentation in the HSV color space by designing a two-channel network, 10 well-known CNN architectures (EfficientNet, MobileNetV3, VGG-19, Xception, InceptionV3, ResNet50V2, VGG-16, NASNetLarge, InceptionResNetV2, and DenseNet201) were employed for feature extraction of different data classes. Of these 10 models, DenseNet201 achieved the best performance in diagnosis and classification. Finally, a model was developed and proposed based on this state-of-the-art technology. This deep learning-based model attained an accuracy, sensitivity, and specificity of 99.85, 99.52, and 99.89%, respectively. The proposed method may help to distinguish ALL from benign cases. This model is also able to assist hematologists and laboratory personnel in diagnosing ALL subtypes and thus determining the treatment protocol associated with these subtypes. The proposed data set is available at https://www.kaggle.com/mehradaria/leukemia and the implementation (source code) of proposed method is made publicly available at https://github.com/MehradAria/ALL-Subtype-Classification.

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.

Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The first is to develop a deep learning framework capable of automating and accurately classifying malaria parasites using microscopic images of thin and thick peripheral blood smears. The second is to report which of the two peripheral blood smears is the most appropriate for use in accurately detecting malaria parasites in peripheral blood smears. Finally, we evaluate the performance of our proposed model with commonly used transfer learning models. We proposed a convolutional neural network capable of accurately predicting the presence of malaria parasites using microscopic images of thin and thick peripheral blood smears. Model evaluation was carried out using commonly used evaluation metrics, and the outcome proved satisfactory. The proposed model performed better when thick peripheral smears were used with accuracy, precision, and sensitivity of 96.97%, 97.00%, and 97.00%. Identifying the most appropriate peripheral blood smear is vital for improved accuracy, rapid smear preparation, and rapid diagnosis of patients, especially in regions where malaria is endemic.

The computer and the digital camera have given proven opportunities to improve the hematology research and education with patient service. Peripheral Blood Smear (PBS) images of high quality can be obtained quickly and smoothly from the Peripheral Blood Smear with the help of a modern, high resolution digital camera and a high quality microscope. A PBS or blood film is a thin layer of blood coated on a microscope slide. PBS are usually examined to analyze the blood related problems and occasionally, to find parasites within the blood. PBS image examination is a part of the daily work of every testing laboratory. The manual examination of these images is difficult, takes more time and faces human intervention and observation error. This has motivated researchers to develop different algorithms and methods to automate peripheral blood smear image analysis. The interest of computer aided decision making has been identified in many medical applications such as automatic detection, classification and analysis of objects in hematological cytology. Image analysis itself consists of a sequence of steps consisting of image segmentation, features extraction and selection and pattern classification. The aim of this review article is to summarize the qualitative abnormalities of the blood cells, viz., Red Blood Cell, White blood Cell and Platelets. This aids the researcher, for ease interpretation and common diagnosis of the peripheral blood smear images. Also, this helps the researchers to propose more relevant image processing and machine learning tools for developing a complete automated PBS analysis system that can reduce the time spent for slide examination.KeywordsMorphological abnormalitiesHemoglobinRBCLeukocytes

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.

Classification of blood cells from Peripheral Blood Smear (PBS) images is a crucial step to diagnose blood-related disorders such as leukemia, anemia, an infection, cancer, and polycythemia. In blood cell-based analysis, the hematologists always make a decision based on the total number of cells, their morphology, and distribution using a microscope. Hematology analyzer, flow cytometry provide reliable and exact Complete Blood Count (CBC) indicating abnormalities in the blood smear slide. The methods being used are very expensive, timeconsuming, require manual intervention and not accessible in many medical centers. Therefore there is a necessity for an automatic, inexpensive and robust technique to detect various types of diseases from any PBS images. The automatic classification model improves the hematological procedures, quickens the diagnosis process and enhances the accuracy of the evaluation process. Thus in this paper, we used a semi-automatic method to segment and classify blood cells into White Blood cell (WBC) and Red Blood Cell (RBC). Texture features of a cell are extracted using Gray Level Co-occurrence Matrix (GLCM) and fed to the classifiers like Naive Bayes classifier, K-nearest neighbors, decision tree, K-means clustering, random forest, logistic regression, ANN and SVM. The performance parameters are compared and found that the logistic regression is best suited for the work with the 97% accuracy.

Background/Objectives: Acute Lymphoblastic Leukemia (ALL) poses significant diagnostic challenges due to its ambiguous symptoms and the limitations of conventional methods like bone marrow biopsies and flow cytometry, which are invasive, costly, and time-intensive. Methods: This study introduces Neuro-Bridge-X, a novel neuro-symbolic hybrid model designed for automated, explainable ALL diagnosis using peripheral blood smear (PBS) images. Leveraging two comprehensive datasets, ALL Image (3256 images from 89 patients) and C-NMC (15,135 images from 118 patients), the model integrates deep morphological feature extraction, vision transformer-based contextual encoding, fuzzy logic-inspired reasoning, and adaptive explainability. To address class imbalance, advanced data augmentation techniques were applied, ensuring equitable representation across benign and leukemic classes. The proposed framework was evaluated through 5-fold cross-validation and fixed train-test splits, employing Nadam, SGD, and Fractional RAdam optimizers. Results: Results demonstrate exceptional performance, with SGD achieving near-perfect accuracy (1.0000 on ALL, 0.9715 on C-NMC) and robust generalization, while Fractional RAdam closely followed (0.9975 on ALL, 0.9656 on C-NMC). Nadam, however, exhibited inconsistent convergence, particularly on C-NMC (0.5002 accuracy). A Meta-XAI controller enhances interpretability by dynamically selecting optimal explanation strategies (Grad-CAM, SHAP, Integrated Gradients, LIME), ensuring clinically relevant insights into model decisions. Conclusions: Visualizations confirm that SGD and RAdam models focus on morphologically critical features, such as leukocyte nuclei, while Nadam struggles with spurious attributions. Neuro-Bridge-X offers a scalable, interpretable solution for ALL diagnosis, with potential to enhance clinical workflows and diagnostic precision in oncology.

This article presents a new model, ALL-Net, for the detection of acute lymphoblastic leukemia (ALL) using a custom convolutional neural network (CNN) architecture and explainable Artificial Intelligence (XAI). A dataset consisting of 3,256 peripheral blood smear (PBS) images belonging to four classes-benign (hematogones), and the other three Early B, Pre-B, and Pro-B, which are subtypes of ALL, are utilized for training and evaluation. The ALL-Net CNN is initially designed and trained on the PBS image dataset, achieving an impressive test accuracy of 97.85%. However, data augmentation techniques are applied to augment the benign class and address the class imbalance challenge. The augmented dataset is then used to retrain the ALL-Net, resulting in a notable improvement in test accuracy, reaching 99.32%. Along with accuracy, we have considered other evaluation metrics and the results illustrate the potential of ALLNet with an average precision of 99.35%, recall of 99.33%, and F1 score of 99.58%. Additionally, XAI techniques, specifically the Local Interpretable Model-Agnostic Explanations (LIME) algorithm is employed to interpret the model's predictions, providing insights into the decision-making process of our ALL-Net CNN. These findings highlight the effectiveness of CNNs in accurately detecting ALL from PBS images and emphasize the importance of addressing data imbalance issues through appropriate preprocessing techniques at the same time demonstrating the usage of XAI in solving the black box approach of the deep learning models. The proposed ALL-Net outperformed EfficientNet, MobileNetV3, VGG-19, Xception, InceptionV3, ResNet50V2, VGG-16, and NASNetLarge except for DenseNet201 with a slight variation of 0.5%. Nevertheless, our ALL-Net model is much less complex than DenseNet201, allowing it to provide faster results. This highlights the need for a more customized and streamlined model, such as ALL-Net, specifically designed for ALL classification. The entire source code of our proposed CNN is publicly available at https://github.com/Abhiram014/ALL-Net-Detection-of-ALL-using-CNN-and-XAI.

Leukemia is a blood cancer originating in the bone marrow that leads to the production of abnormal blood cells, impacting cell functionality and efficiency. According to Globocan, statistics for 2022 show that Viet Nam has 180,480 new cancer cases and 120,184 deaths, of which leukemia has 5,789 new cases, ranking 8th among cancer types (accounting for 3.2%) and 4,330 deaths, ranking 6th in mortality rate among cancer types (accounting for 3.6%). Depending on the progression rate, leukemia can be identified as either acute or chronic. Acute leukemia affects blood cells in the early stages of development, causing the disease to become more severe, while chronic leukemia affects more developed blood cells, and the disease tends to develop slowly. Another way to classify leukemia is by the specific blood cell type it targets, either myeloid or lymphoid cells. Understanding the affected cell type is essential for diagnosis and treatment planning, as it influences disease behavior and response to treatment. This paper focuses on identifying and classifying ALL (Acute Lymphoblastic Leukemia) blasts in the most prevalent childhood cancer type using transfer learning of the Alexnet network model in Matlab software. Diagnosing ALL disease using peripheral blood smear (PBS) images helps screen and treat the disease early, contributing to improving treatment effectiveness for patients. Laboratory users often face challenges when examining PBS images, as the non-specific signs and symptoms of ALL can result in frequent misdiagnoses and diagnostic errors. The research presents a model for analyzing peripheral blood smear images, aimed at identifying ALL cell types and distinguishing among its malignant subtypes. The ALexnet model will be trained in 3 different ways including: Train with no parameters (Training a Neural Network from Scratch), train the pretrained network but change the fullyConnected layer at the end and train the pretrained network but change the 14th layer onwards. The results show that the model using the second training method achieves the best performance when reaching 98.664% accuracy on the validation set, while methods 1 and 3 are 92.292% and 89.825% respectively. Finally, the second model is saved to design the user application using AppDesigner.

Iron Deficiency Anemia (IDA) is the nutritional disorder that occurs when the body does not contain enough iron, an essential component of hemoglobin (Hb). The World Health Organization (WHO) estimated that IDA is the main cause of anemia in 1.62 billion cases worldwide [1]. Although IDA rarely results in death, it has significant adverse impacts on human health. During diagnosis, the hemoglobin indices show low mean corpuscular hemoglobin and mean corpuscular hemoglobin volume. On Peripheral Blood Smear (PBS) images viewed under a microscope by hematologists, IDA shows hypochromic and microcytic red cells. The purpose of the proposed research is to develop a computer-aided system that will allow hematologists to diagnose and detect diseases more accurately and quickly, therefore saving them time and effort. In order to diagnose or detect clinical disorders, non-invasive techniques like machine learning algorithms are employed. This work aims to identify IDA by utilizing the RetinaNet-Disentangled Dense Object Detector (DDOD) to localize hypochromic microcytes in PBS images. To the best of our knowledge, this is the first work using the object detection technique to detect IDA based on the Red Blood Cell (RBC) morphology. We carried out an extensive quantitative and qualitative evaluation of the model. Additionally, a comparison was made between the performance of our model and other object detection models. The results showed that our approach outperformed state-of-the-art techniques, with a mean average precision that was more than 8% higher.

Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly. To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses a marker-based watershed algorithm and peak local maxima. The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was the same, i.e. 94% but the Structural Similarity Index Metric (SSIM) and recall of HSV were better than other two. The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.

Iron Deficiency Anaemia (IDA) is the most prevalent form of anaemia, affecting 24.8% of the global population. An examination of the complete blood count (CBC) is performed to determine general health and the presence of illnesses. Accurate and timely diagnosis of IDA is essential for proper treatment, yet traditional methods can be time-consuming and costly. This study uses machine learning and computer vision techniques for the automatic identification of hypochromic microcytes from Peripheral Blood Smear (PBS) images to improve IDA diagnosis. Two approaches were implemented: first, a ResNet50 model was used to classify PBS images as Normal or IDA; second, the YOLOv7 object detection model was employed to localize hypochromic microcytes within the images. The YOLOv7 model was tested on 17 images containing 425 instances of hypochromic microcytes and demonstrated superior performance, achieving a test mean Average Precision (mAP) of 89% with faster inference times than ResNet50. By providing localized detection of hypochromic microcytes, YOLOv7 enhances diagnostic accuracy and speed compared to image-level classification. This study highlights the potential of object detection models for improving automated anaemia diagnosis, with implications for faster and more cost-effective healthcare solutions.

Leukemia is a form of blood cancer that affects the body’s ability to fight against infection. Every year, about 1 million cases of leukemia are reported with an increased mortality rate that can cause a delay in diagnosis and treatment of leukemia. Conventionally, Acute Lymphoblastic Leukemia is identified by manual counting of cells from a peripheral blood smear or by bone marrow aspiration. However, this method is time consuming and prone to human errors. To prevail over this, various automated techniques were introduced which are faster, reliable and cheaper than the manual methods. This paper focuses on how image processing techniques can be used to identify Acute Lymphoblastic Leukemia (ALL) from the peripheral blood smear images.

In the domain of histology, discovering the population of white blood cells (WBC) in blood smears helps to recognise destructive diseases. Standard tests performed in hematopathological laboratories by human experts on the blood samples of precarious cases such as leukaemia are time-consuming processes, less accurate and totally depending upon the expertise of the technicians. In order to get the advantage of faster analysis time and perfect partitioning at clumps, an algorithm is proposed in this paper that automatically identifies the counting of lymphocytes present in peripheral blood smear images containing acute lymphoblastic leukaemia (ALL) that performs lymphocytes segmentation by fuzzy C-means (FCM) clustering. Afterward, neighbouring and touching cells in cell clumps are individuated by the watershed transform (WT), and then morphological operators are applied to bring out the cells into an appropriate format in accordance with feature extraction. The extracted features are thresholded to eliminate the regions other than lymphocytes. The algorithm ensures 98.52% of accuracy in counting lymphocytes by examining 80 blood-smear image samples of the ALL-IDB1 dataset. The research works in showing this kind of improved accuracy facilitates in identifying leukaemia on starting stages for uncomplicated healing.

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-19