Blood cell image segmentation and classification: a systematic review

PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e1813 - e1813

Опубликована: Фев. 2, 2024

Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology concentration of blood elements, specifically white cells (WBC) red (RBC). Accurate efficient diagnosis these conditions significantly depends on expertise hematologists pathologists. To assist pathologist diagnostic process, there has been growing interest utilizing computer-aided (CAD) techniques, particularly those using medical image processing machine learning algorithms. Previous surveys this domain have narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. This survey aims provide comprehensive systematic review existing literature research work field analysis deep techniques. It focuses techniques algorithms excel morphological characterization WBCs RBCs. The is structured cover four main areas: methodologies, descriptive selection, parameters, selection for Our reveals several interesting trends preferences among researchers. Regarding approximately 50% related WBC 60% RBC opted manually obtaining images rather than predefined dataset. When it comes 45% previous chose ALL-IDB dataset, while significant 73% researchers focused decided obtain from institutions instead datasets. In terms features were most popular, being chosen 55% 80% studies respectively. accuracy blood-related can be enhanced through effective use CAD which evolved considerably recent years. provides broad in-depth employed, utilization selection. inconsistency suggests need standardized, high-quality datasets strengthen capabilities further. Additionally, popularity indicates future could further explore innovate direction.

Язык: Английский

---

MultiFeNet: Multi‐scale feature scaling in deep neural network for the brain tumour classification in MRI images

International Journal of Imaging Systems and Technology, Год журнала: 2023, Номер 34(1)

Опубликована: Авг. 25, 2023

Abstract One of the most fatal and prevalent diseases central nervous system is a brain tumour. Different subgrades exist for each type tumour because broad variety tumours pathologies. Manual diagnosis may be error‐prone time‐consuming, both which are becoming more challenging as medical community's workload grows. There need automatic diagnosis. In this study, we have proposed deep learning model (MultiFeNet) based on convolutional neural network classification tumours. MultiFeNet uses multi‐scale feature scaling extraction in magnetic resonance imaging (MRI) images. Multi‐scaling helps to learn better representation MRI image enhanced performance. To evaluate model, 3064 scans three distinct categories (meningiomas, gliomas pituitary tumours) were used. The obtained 96.4% sensitivity, F1‐score, precision accuracy benchmark Figshare dataset. addition, an ablation study conducted with objective evaluating role multi‐scaling

Язык: Английский

---

CoTCoNet: An optimized coupled transformer-convolutional network with an adaptive graph reconstruction for leukemia detection

Computers in Biology and Medicine, Год журнала: 2024, Номер 179, С. 108821 - 108821

Опубликована: Июль 6, 2024

Язык: Английский

---

Human crowd behaviour analysis based on video segmentation and classification using expectation–maximization with deep learning architectures

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Март 16, 2024

Язык: Английский

---

SERS and Machine Learning-Enabled Liquid Biopsy: A Promising Tool for Early Detection and Recurrence Prediction in Acute Leukemia

ACS Omega, Год журнала: 2025, Номер unknown

Опубликована: Март 20, 2025

Acute leukemia (AL), classified as acute myeloid (AML) and lymphocytic (ALL), is a hematologic malignancy caused by the uncontrolled proliferation of leucocytes in bone marrow. Early detection AL crucial for clinical treatment. Detection methods are currently blood tests, marrow imaging, spinal fluid tests. However, these tests have drawbacks, such high cost time consumption. Liquid biopsy using biological fluids or serum an emerging technique noninvasive cancer monitoring. Surface-enhanced Raman spectroscopy (SERS), which enhanced signals interaction plasmonic nanostructures with analyte, highly sensitive specific method simple sample preparation that has been used combination machine learning techniques to analyze liquid biopsy. In this study, we developed SERS-based approach enables accurate classification AML ALL subtypes prediction disease recurrence. SERS spectra samples from 24 healthy individuals, 43 patients, 18 patients were obtained Ag-based substrate clustered hierarchical cluster analysis (HCA). The then three commonly classifiers, namely, support vector (SVM), random forest (RF), k-nearest neighbor (kNN). Our findings demonstrate RF classifier highest accuracy values, 96.1, 95.5, 98.5% classifying groups predicting recurrence ALL, respectively. algorithms represents remarkable advancement realm hematological diagnostics, particularly ALL. This not only facilitates precise differentiation but also introduces novel capability prognosticating

Язык: Английский

---

Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images

Frontiers in Oncology, Год журнала: 2023, Номер 13

Опубликована: Дек. 6, 2023

Acute lymphoblastic leukemia (ALL) poses a significant health challenge, particularly in pediatric cases, requiring precise and rapid diagnostic approaches. This comprehensive review explores the transformative capacity of deep learning (DL) enhancing ALL diagnosis classification, focusing on bone marrow image analysis. Examining ten studies conducted between 2013 2023 across various countries, including India, China, KSA, Mexico, synthesis underscores adaptability proficiency DL methodologies detecting leukemia. Innovative models, notably Convolutional Neural Networks (CNNs) with Cat-Boosting, XG-Boosting, Transfer Learning techniques, demonstrate notable Some models achieve outstanding accuracy, one CNN reaching 100% cancer cell classification. The incorporation novel algorithms like Cat-Swarm Optimization specialized architectures contributes to superior classification accuracy. Performance metrics highlight these achievements, consistently outperforming traditional methods. For instance, Cat-Boosting attains while others hover around 99%, showcasing models' robustness diagnosis. Despite acknowledged challenges, such as need for larger more diverse datasets, findings underscore DL's potential reshaping diagnostics. high numerical accuracies accentuate promising trajectory toward efficient accurate clinical settings, prompting ongoing research address challenges refine optimal integration.

Язык: Английский

---

The Art of YOLOv8 Algorithm in Cancer Diagnosis using Medical Imaging

2021 International Conference on System, Computation, Automation and Networking (ICSCAN), Год журнала: 2023, Номер unknown

Опубликована: Ноя. 17, 2023

Cancer continues to be a global health challenge, demanding innovative solutions improve early detection and treatment outcomes. This research project harnesses the power of deep learning in field medical imaging investigate applicability YOLOv8 (You Only Look Once version 8) algorithm for diagnosing various cancer types, such as Acute Lymphoblastic Leukemia, Cervical, Lung, Colon, Oral, Skin cancers. The algorithm, renowned its real-time object prowess, represents promising candidate automating identification classification cancerous regions within images. study encompasses comprehensive methodology, starting with collection preprocessing diverse well-annotated image datasets. is then fine-tuned trained on these datasets, capitalizing capabilities discern lesions. model's performance undergoes evaluation using established metrics, guaranteeing dependability precision clinical setting. findings this have potential offer insightful information YOLOv8. By bridging gap between cutting-edge technology practice, seeks advance provide foundation more precise, efficient, accessible methods. Ultimately, goal enhance diagnosis cancer, offering new possibilities timely intervention improved patient

Язык: Английский

---

Linear programming based computational technique for leukemia classification using gene expression profile

PLoS ONE, Год журнала: 2023, Номер 18(10), С. e0292172 - e0292172

Опубликована: Окт. 9, 2023

Cancer is a serious public health concern worldwide and the leading cause of death. Blood cancer one most dangerous types cancer. Leukemia type that affects blood cell bone marrow. Acute leukemia chronic condition fatal if left untreated. A timely, reliable, accurate diagnosis at an early stage critical to treating preserving patients' lives. There are four leukemia, namely acute lymphocytic myelogenous in extracting, leukemia. Recognizing these cancerous development cells often done via manual analysis microscopic images. This requires extraordinarily skilled pathologist. symptoms might include lethargy, lack energy, pale complexion, recurrent infections, easy bleeding or bruising. One challenges this area identifying subtypes for specialized treatment. Study carried out increase precision assist personalized plans treatment, improve general leukemia-related healthcare practises. In research, we used gene expression data from Curated Microarray Database (CuMiDa). Microarrays ideal studying cancer, however, categorizing pattern microarray information can be challenging. proposed study uses feature selection methods machine learning techniques predict classify CuMiDa (GSE9476). research work utilized linear programming (LP) as machine-learning technique classification. Linear model classifies predicts Bone_Marrow_CD34, Bone Marrow, AML, PB, PBSC CD34. Before using LP model, selected 25 features given dataset 22283 features. These significant were distinguishing The classification accuracy 98.44%.

Язык: Английский

---

Comparative Study of Object Recognition Utilizing Machine Learning Techniques

Опубликована: Май 9, 2024

Язык: Английский

---

Evaluation of the practical application of the category-imbalanced myeloid cell classification model

PLoS ONE, Год журнала: 2025, Номер 20(1), С. e0313277 - e0313277

Опубликована: Янв. 30, 2025

The incidence of acute myeloid leukemia (AML) is increasing annually, and timely diagnostic treatments can substantially improve patient survival rates. AML typing traditionally relies on manual microscopy for classifying counting cells, which time-consuming, laborious, subjective. Therefore, developing a reliable automated model cell classification imperative. This study evaluated the performance five widely-used models largest publicly available bone marrow dataset (BM). However, accuracy significantly affected by imbalance in distribution types. To address this issue, analyzed different Loss functions seven attention mechanisms. When chosen, Swin Transformer V2 was found to perform best. lightweight RegNetX-3.2gf had fewer parameters faster inference speed than V2, its F1 Score only 0.032 lower that V2. Accordingly, strongly recommended practical applications. During evaluation function mechanism, Cost-Sensitive Function (CS) channel mechanism Squeeze-and-Excitation Networks (SE) demonstrated superior performance. optimal (RegNetX-3.2gf + CS SE) achieved an average precision 68.183%, recall 63.722%, 65.155%. exhibited improved compared original results, achieving enhancement 17.183% 10.655% Score. Finally, class activation maps demonstrate our focused cells themselves, especially nucleus when making classifications. It proved reliable. provided important reference application model, promoting development intelligent AML.

Язык: Английский

---