Blood Smear Images Research Articles

StainGAN: Learning a structural preserving translation for white blood cell images.

Analysis of white blood cells in blood smear images plays a vital role in computer-aided diagnosis for the analysis and treatment of many diseases. However, different techniques for blood smear preparation result in images with large appearance variations, which limits the performance of large-scale machine learning algorithms. In this paper, we propose StainGAN, an image translation framework to transform the conventional Wright-stained white blood cell images into their rapidly-stained counterpart. Moreover, we designed a cluster-based learning strategy that does not require manual annotations and a multi-scale discriminator that incorporates a richer hierarchy of the spatial context to generate sharper images with better semantic consistency. Experimental results on multiple real-world datasets prove the effectiveness of our proposed framework. Moreover, we show that the transformed images from StainGAN can be used to boost the downstream segmentation performance under the label-limiting scenario.

AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images

Malaria is a significant public health concern, with ∼95% of cases occurring in Africa, but accurate and timely diagnosis is problematic in remote and low-income areas. Here, we developed an artificial intelligence-based object detection system for malaria diagnosis (AIDMAN). In this system, the YOLOv5 model is used to detect cells in a thin blood smear. An attentional aligner model (AAM) is then applied for cellular classification that consists of multi-scale features, a local context aligner, and multi-scale attention. Finally, a convolutional neural network classifier is applied for diagnosis using blood-smear images, reducing interference caused by false positive cells. The results demonstrate that AIDMAN handles interference well, with a diagnostic accuracy of 98.62% for cells and 97% for blood-smear images. The prospective clinical validation accuracy of 98.44% is comparable to that of microscopists. AIDMAN shows clinically acceptable detection of malaria parasites and could aid malaria diagnosis, especially in areas lacking experienced parasitologists and equipment.

Peripheral Blood Smear Analysis Using Automated Computer-Aided Diagnosis System to Identify Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a cancer of the myeloid line of cells caused due to the rapid increase of abnormal cells that later interfere with healthy cells. One of the main reasons for the increase in mortality is the cost of the devices used for the determination and late diagnosis. The most effective treatment option can be provided by accurate medical diagnosis. Automated segmentation of blood smear images plays a crucial role in the identification of the AML. This article proposes a new computer-aided diagnosis model to segment the blood smear images and identifies the stage of AML. The methodology presented in this work consists of various stages: Image acquisition, image segmentation, feature extraction/selection, and classification. The model is trained using 800 blood smear images collected from Kasturba Medical College Manipal, and 200 images collected from the dataset of microscopic peripheral blood cell images for the development of automatic recognition systems. The model is tested on 500 images. A novel algorithm is designed to accurately segment the blood smear images to identify AML and its stages. The segmentation algorithm addresses critical issues in blast cell detection, including identifying the blast cell and extracting the cytoplasm of the cell without involving manual intervention. It can identify the multiple lobes and the nucleated red blood cells (NRBCs), separate the overlapped erythrocytes from white blood cells (WBCs), and discover the presence of Auer rods and granules. The feature selection is performed using the InfoGainAttributeEval and the ranker search method. This article compares the performance of the various machine learning algorithms exploited for the classification of different types of cells and hence determines AML. The model successfully differentiated between NRBC and WBC with an accuracy of 99.81%. The model obtained a classification accuracy of 99.48 %. It achieved a prediction accuracy of 99.2% while predicting the unknown stage of AML. The algorithm’s efficiency proves that it can be used by pathologists to form a prognosis.

The evaluation of denoising techniques on microscopic blood smear images of Acute Lymphoblastic Leukemia (ALL)

Preprocessing is the first step in the image processing for any digital image before it goes further step. Denoising techniques are one of the important techniques used in preprocessing. The digitized microscopic blood smear image contains unwanted noise due to poor illumination, electronic interference, different variation in lighting condition etc. The processing of these images without the filtering techniques, can produce inaccurate results in the further step of image processing such as segmentation, feature extraction and classification. So, it is necessary to preprocess the images with proper techniques for each type of preprocessing. In this paper, we have tried to evaluate the different types of the filtering techniques on the blood smear images of Acute Lymphoblastic Leukemia (ALL) for the removal of noise. We have reviewed many research work which have used various filtering techniques used for removal of noise.

An automated malaria cells detection from thin blood smear images using deep learning.

Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic examination is the gold standard for malaria diagnosis, where hundreds of millions of blood films are examined annually. However, this method's effectiveness depends on the trained microscopist's skills. With the increasing interest in applying deep learning in malaria diagnosis, this study aims to determine the most suitable deep-learning object detection architecture and their applicability to detect and distinguish red blood cells as either malaria-infected or non-infected cells. The object detectors Yolov4, Faster R-CNN, and SSD 300 are trained with images infected by all five malaria parasites and from four stages of infection with 80/20 train and test data partition. The performance of object detectors is evaluated, and hyperparameters are optimized to select the best-performing model. The best-performing model was also assessed with an independent dataset to verify the models' ability to generalize in different domains. The results show that upon training, the Yolov4 model achieves a precision of 83%, recall of 95%, F1-score of 89%, and mean average precision of 93.87% at a threshold of 0.5. Conclusively, Yolov4 can act as an alternative in detecting the infected cells from whole thin blood smear images. Object detectors can complement a deep learning classification model in detecting infected cells since they eliminate the need to train on single-cell images and have been demonstrated to be more feasible for a different target domain.

ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images.

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.

Feature Masking on Non-overlapping Regions for Detecting Dense Cells in Blood Smear Image.

Detecting cells in blood smear images is of great significance for automatic diagnosis of blood diseases. However, this task is rather challenging, mainly because there are dense cells that are often overlapping, making some of the occluded boundary parts invisible. In this paper, we propose a generic and effective detection framework that exploits non-overlapping regions (NOR) for providing discriminative and confident information to compensate the intensity deficiency. In particular, we propose a feature masking (FM) to exploit the NOR mask generated from the original annotation information, which can guide the network to extract NOR features as supplementary information. Furthermore, we exploit NOR features to directly predict the NOR bounding boxes (NOR BBoxes). NOR BBoxes are combined with the original BBoxes for generating one-to-one corresponding BBox-pairs that are used for further improving the detection performance. Different from the non-maximum suppression (NMS), our proposed non-overlapping regions NMS (NOR-NMS) uses the NOR BBoxes in the BBox-pairs to calculate intersection over union (IoU) for suppressing redundant BBoxes, and consequently retains the corresponding original BBoxes, circumventing the dilemma of NMS. We conducted extensive experiments on two publicly available datasets, with positive results demonstrating the effectiveness of the proposed method against existing methods.

Malarial Diagnosis with Deep Learning and Image Processing Approaches

Malaria is a mosquito-borne disease that has killed an estimated a half-a-million people worldwide since 2000. It may be time consuming and costly to conduct thorough laboratory testing for malaria, and it also requires the skills of trained laboratory personnel. Additionally, human analysis might make mistakes. Integrating denoising and image segmentation techniques with Generative Adversarial Network (GAN) as a data augmentation technique can enhance the performance of diagnosis. Various deep learning models, such as CNN, ResNet50, and VGG19, for recognising the Plasmodium parasite in thick blood smear images have been used. The experimental results indicate that the VGG19 model performed best by achieving 98.46% compared to other approaches. This study demonstrates the potential of artificial intelligence to improve the speed and precision of pathogen detection which is more effective than manual analysis.

ACCURATE CELL SEGMENTATION IN BLOOD SMEAR IMAGES BASED ON COLOR ANALYSIS AND CNN MODELS

Abstract. Nowadays, automated blood cell evaluation play a major role in the classification and diagnosis of diseases. Despite the many possible ways to segment blood cells, the recognition efficiency remains insufficient, especially when different cell types overlap. Also, one should not forget about the cells structure complexity. Image segmentation and image classification are the main stages of this problem. At the same time, segmentation of blood smear images is considered the most important stage in automated disease detection systems. Often cell segmentation in blood smear images is performed as a separate mapping for white blood cells and red blood cells. We propose another problem statement that uses the capabilities of supervised and unsupervised CNNs for the semantic segmentation of objects of different sizes and shapes. CNN has encoder-decoder architecture and builds a pseudo-color map. We tested several CNN models using different color spaces converting initial images from RGB to Lab, HSV and CMYK color spaces and obtained promising experimental results for several microscopic datasets such as CellaVision DM96, All-IDB and Blood Cell Detection.

An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images.

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.

Malaria Cell Classification Using Transfer Learning

Abstract: In recent years, machine learning techniques, particularly transfer learning, have shown promise in automating malaria cell classification using digital images of blood smears. Transfer learning involves leveraging pre-trained neural networks to extract relevant features from large datasets and applying them to smaller, specialized datasets for improved performance. This abstract provides an overview of the concept of malaria cell classification using transfer learning. It highlights the advantages of using transfer learning, including reduced training time and improved classification accuracy, while addressing challenges such as dataset bias and model interpretability. Further research and development in this area could potentially contribute to the automation and scalability of malaria diagnosis, particularly in resource-limited settings. This study describes a method for classifying malaria cells using convolutional neural networks. It has been evaluated using a standard dataset obtained from the National Library of Medicine, which consists of 27,000microscopic pictures. The accuracy of the neural network has been improved while the total loss has been reduced using the Adam optimizer. Overfitting has been avoided by using dropout regression

Mask R-CNN Based Segmentation and Classification of Blood Smear Images

Mask R-CNN Based Segmentation and Classification of Blood Smear Images

Automated counting of white blood cells in thin blood smear images

Blood cell counting plays a crucial role in clinical diagnosis to evaluate the overall health condition of an individual. Traditionally, blood cells are manually counted using a hemocytometer; however, this task has been found to be time-consuming and error-prone. Recently, machine learning-based approaches have been employed to effectively automate counting tasks. In this work, the fifth version of the ‘you only look once’ (YOLOv5) object detection method was adopted to automatically detect and count white blood cells (WBCs) in porcine blood smear images. YOLOv5 was chosen because of its speed and accuracy. The dataset used in this study was collected specifically for this WBC counting task. Our experimental results exhibit the high speed and efficiency of YOLOv5 in detecting and counting WBCs, having obtained an accuracy of 89.25% and a mean average precision at 0.5 intersection over union threshold (mAP 0.5) of 99%.

Ghost-ResNeXt: An Effective Deep Learning Based on Mature and Immature WBC Classification

White blood cells (WBCs) must be evaluated to determine how well the human immune system performs. Abnormal WBC counts may indicate malignancy, tuberculosis, severe anemia, cancer, and other serious diseases. To get an early diagnosis and to check if WBCs are abnormal or normal, one needs to examine the numbers and determine the shape of the WBCs. To address this problem, computer-aided procedures have been developed because hematologists perform this laborious, expensive, and time-consuming process manually. Resultantly, a powerful deep learning model was developed in the present study to categorize WBCs, including immature WBCs, from the images of peripheral blood smears. A network based on W-Net, a CNN-based method for WBC classification, was developed to execute the segmentation of leukocytes. Thereafter, significant feature maps were retrieved using a deep learning framework built on GhostNet. Then, they were categorized using a ResNeXt with a Wildebeest Herd Optimization (WHO)-based method. In addition, Deep Convolutional Generative Adversarial Network (DCGAN)-based data augmentation was implemented to handle the imbalanced data issue. To validate the model performance, the proposed technique was compared with the existing techniques and achieved 99.16%, 99.24%, and 98.61% accuracy levels for Leukocyte Images for Segmentation and Classification (LISC), Blood Cell Count and Detection (BCCD), and the single-cell morphological dataset, respectively. Thus, we can conclude that the proposed approach is valuable and adaptable for blood cell microscopic analysis in clinical settings.

Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.

Acute lymphoblastic leukemia image segmentation based on modified HSV model

Image segmentation is a critical step in computer-aided diagnosis that could speed up Leukemia detection. Leukemia is a cancer of the blood that has a reputation for being particularly lethal. Based on the immunohistochemical method, the leukocytes can be manually counted in a stained peripheral blood smear image to detect Acute Lymphoblastic Leukemia (ALL). Regrettably, the manual diagnosis process takes about 3 to 24 hours to complete, which is insufficient. This paper introduced a new and straightforward ALL image segmentation approach based on color image transformation. First, Leukemia, ALL-IDB1, ALL-IDB2, and ALL image datasets were used in this paper. The Leukemia dataset includes 208 ALL-IDB1 and ALL-IDB2 images, while The ALL dataset has 3256 images. Next, we use the HSV model to transform ALL images. In addition, we modified the HSV model by pre-processing the saturation channel for better results. Then, the pre-processed images were segmented based on a fixed threshold. After that, various metrics are utilized to measure the output of the proposed method. Finally, the proposed methodology is compared to currently used benchmarks. The proposed method outperforms previous approaches regarding accuracy, specificity, sensitivity, and time. In addition, results show that the proposed technique improves performance measures significantly.

An Intelligent Approach for Blood Cell Detection Employing Faster RCNN

A CBC (complete blood count) is an essential component of a thorough medical evaluation. The practice of medicine has had a significant impact on popular methodologies, such as manual counting and automated analyzers. The shape of blood cells and other cellular characteristics are highly sensitive to contamination levels in the human body. Tiny blood cell images are studied to spot disease and deviations from the norm that may indicate internal contamination. Correct cell segmentation enables more precise and powerful disease detection. Examining blood cells under a microscope is an important part of any pathological investigation. It emphasizes the examination of the correct malady after pinpointing its precise location and then ranking its anomalies, which plays a crucial role in determining the nature of a patient's condition, planning treatment, and evaluating the outcome of that treatment. Initially the complete blood count (CBC) test of patient is carried out if the report suggested that there is abnormality in the blood than that blood is poured on strip and blood is stained out through addition of coagulant. Expert hematologists are in short supply, particularly in underdeveloped nations, and are frequently overwhelmed. To help them with their burden, we provide a unique method for the automated assessment of family disease using artificial intelligence on blood smear images in this paper. For object detection to be useful in real life, it needs to be quick and good at finding different things in an image. There have been many improvements made to object detection, such as Convolutional Neural Network and the family of R-CNN. In our research, we employ Faster R-CNN to look for RBCs, WBCs, and Platelets in the Blood Cell Count (CBC) and Detection dataset. The number, shape, and other meta-information, or any abnormalities in the different parts of blood, could help find problems and/or diseases like leukemia, anemia, lymphoma, sickle cell disease, thrombocytopenia, leukopenia etc., early on. Our model is accurate enough to find a bounding box on the blood parts. A box is drawn along with their name around the various components of the blood smear slide.

White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization.

White blood cells (WBCs) constitute an essential part of the human immune system. The correct identification of WBC subtypes is critical in the diagnosis of leukemia, a kind of blood cancer defined by the aberrant proliferation of malignant leukocytes in the bone marrow. The traditional approach of classifying WBCs, which involves the visual analysis of blood smear images, is labor-intensive and error-prone. Modern approaches based on deep convolutional neural networks provide significant results for this type of image categorization, but have high processing and implementation costs owing to very large feature sets. This paper presents an improved hybrid approach for efficient WBC subtype classification. First, optimum deep features are extracted from enhanced and segmented WBC images using transfer learning on pre-trained deep neural networks, i.e., DenseNet201 and Darknet53. The serially fused feature vector is then filtered using an entropy-controlled marine predator algorithm (ECMPA). This nature-inspired meta-heuristic optimization algorithm selects the most dominant features while discarding the weak ones. The reduced feature vector is classified with multiple baseline classifiers with various kernel settings. The proposed methodology is validated on a public dataset of 5000 synthetic images that correspond to five different subtypes of WBCs. The system achieves an overall average accuracy of 99.9% with more than 95% reduction in the size of the feature vector. The feature selection algorithm also demonstrates better convergence performance as compared to classical meta-heuristic algorithms. The proposed method also demonstrates a comparable performance with several existing works on WBC classification.

An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images

Acute lymphoblastic leukemia (ALL) is the most serious type of leukemia that develops because it causes an abnormal increase in the production of immature white blood cells in the bone marrow. ALL spreads rapidly in children's bodies and leads to their death. The main objective of this paper is to introduce an enhanced methodology based on the k-means clustering algorithm for classifying all subtypes of ALL. Image preprocessing is the first step of the proposed methodology. For obtaining the images' descriptive features, feature extraction is the second step that has been used. To select the most vital features that can characterize the histology of the blood cells, Enhanced Grey Wolf Optimization (EGWO) algorithm has been used in the third step. Hence, EGWO will start to select the search agents based on certain criteria as the best cluster center by using the k-means clustering algorithm. Several supervised classifiers as Random Forest (RF), K Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) have been compared. The proposed methodology achieves a high degree of accuracy of99.22%,precision of99%,and sensitivity of99%.A comparative study has been established in order to verify the effectiveness of the proposed methodology.

Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics.

Dengue fever, also known as break-bone fever, can be life-threatening. Caused by DENV, an RNA virus from the Flaviviridae family, dengue is currently a globally important public health problem. The clinical methods available for dengue diagnosis require skilled supervision. They are manual, time-consuming, labor-intensive, and not affordable to common people. This paper describes a method that can support clinicians during dengue diagnosis. It is proposed to automate the peripheral blood smear (PBS) examination using Artificial Intelligence (AI) to aid dengue diagnosis. Nowadays, AI, especially Machine Learning (ML), is increasingly being explored for successful analyses in the biomedical field. Digital pathology coupled with AI holds great potential in developing healthcare services. The automation system developed incorporates a blob detection method to detect platelets and thrombocytopenia from the PBS images. The results achieved are clinically acceptable. Moreover, an ML-based technique is proposed to detect dengue from the images of PBS based on the lymphocyte nucleus. Ten features are extracted, including six morphological and four Gray Level Spatial Dependance Matrix (GLSDM) features, out of the lymphocyte nucleus of normal and dengue cases. Features are then subjected to various popular supervised classifiers built using a ten-fold cross-validation policy for automated dengue detection. Among all the classifiers, the best performance was achieved by Support Vector Machine (SVM) and Decision Tree (DT), each with an accuracy of 93.62%. Furthermore, 1000 deep features extracted using pre-trained MobileNetV2 and 177 textural features extracted using Local binary pattern (LBP) from the lymphocyte nucleus are subjected to feature selection. The ReliefF selected 100 most significant features are then fed to the classifiers. The best performance was attained using an SVM classifier with 95.74% accuracy. With the obtained results, it is evident that this proposed approach can efficiently contribute as an adjuvant tool for diagnosing dengue from the digital microscopic images of PBS.