A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification

Diagnostics, Год журнала: 2023, Номер 13(18), С. 3007 - 3007

Опубликована: Сен. 20, 2023

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection vitally important to save many lives. Brain tumors can be divided into several categories depending on kind, place origin, pace development, stage progression; as a result, tumor classification crucial for targeted therapy. segmentation aims delineate accurately areas A specialist with thorough understanding illnesses needed manually identify proper type tumor. Additionally, processing images takes time tiresome. Therefore, automatic techniques are required speed up enhance diagnosis Tumors quickly safely detected by scans using imaging modalities, including computed tomography (CT), magnetic resonance (MRI), others. Machine learning (ML) artificial intelligence (AI) have shown promise in developing algorithms that aid utilizing various modalities. The right method must used precisely classify patients treatment. This review describes multiple types tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine techniques, deep learning, through transfer study In this study, we attempted synthesize modalities automatically computer-assisted methodologies characterization ML DL frameworks. Finding current problems engineering currently use predicting future paradigm other goals article.

Язык: Английский

---

Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system

Biomedical Signal Processing and Control, Год журнала: 2023, Номер 86, С. 105119 - 105119

Опубликована: Июнь 18, 2023

Язык: Английский

---

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Diagnostics, Год журнала: 2023, Номер 13(19), С. 3147 - 3147

Опубликована: Окт. 7, 2023

Skin lesions are essential for the early detection and management of a number dermatological disorders. Learning-based methods skin lesion analysis have drawn much attention lately because improvements in computer vision machine learning techniques. A review most-recent classification, segmentation, is presented this survey paper. The significance healthcare difficulties physical inspection discussed state-of-the-art papers targeting classification then covered depth with goal correctly identifying type from dermoscopic, macroscopic, other image formats. contribution limitations various techniques used selected study papers, including deep architectures conventional methods, examined. looks into focused on segmentation that aimed to identify precise borders classify them accordingly. These make it easier conduct subsequent analyses allow measurements quantitative evaluations. paper discusses well-known algorithms, deep-learning-based, graph-based, region-based ones. difficulties, datasets, evaluation metrics particular also discussed. Throughout survey, notable benchmark challenges, relevant highlighted, providing comprehensive overview field. concludes summary major trends, potential future directions detection, aiming inspire further advancements critical domain research.

Язык: Английский

---

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Applied Sciences, Год журнала: 2024, Номер 14(2), С. 923 - 923

Опубликована: Янв. 22, 2024

Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing segmentation, focusing on lung CT scan chest X-ray datasets. Best-cost values reveal PSO algorithm’s performance, HE demonstrating significant stabilization enhanced convergence, particularly complex images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, Jaccard, show substantial improvements preprocessing, emphasizing its impact accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, K-means, confirm competitiveness PSO-HE approach, especially The also underscores positive influence clarity precision. These findings highlight promise approach advancing accuracy reliability pave way further method integration to enhance this critical healthcare application.

Язык: Английский

---

A deep autoencoder approach for detection of brain tumor images

Computers & Electrical Engineering, Год журнала: 2022, Номер 102, С. 108238 - 108238

Опубликована: Июль 27, 2022

Язык: Английский

---

A robust classification of brain tumor disease in MRI using twin-attention based dense convolutional auto-encoder

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 92, С. 106088 - 106088

Опубликована: Фев. 17, 2024

Язык: Английский

---

Deep Learning Techniques for the Classification of Brain Tumor: A Comprehensive Survey

IEEE Access, Год журнала: 2023, Номер 11, С. 113050 - 113063

Опубликована: Янв. 1, 2023

Researchers have given immense consideration to unsupervised approaches because of their tendency for automatic feature generation and excellent performance with a reduced error margin. Deep learning (DL) models are emerging as vital methods image analysis in medical fields, such classification, segmentation, reconstruction. relies on hierarchical features data representation, making it superior its antecedent. efficiently discover descriptive information about the optimal representation various brain tumors when applied tumor classification from MRI. Despite efforts, there remains gap current literature inclusive recently developed deep learning-based methods. The study attempts fill this by briefly reviewing state art segmentation while focusing proposed survey dedicates itself reviewed automated techniques MRI produce an picture most recent worthy adoption area. conduct surveys techniques, no could be found that has dedicated focus effective approach towards classification. This research begins identifying major classes presenting focused area state-of-the-art approach, method. powerful ability mechanisms been performance, comparison between them is presented encourage applications. Future recommendations directions also drawn up establish pursuable course welcoming widespread potential applications

Язык: Английский

---

Advanced Medical Image Segmentation Enhancement: A Particle Swarm Optimization-Based Histogram Equalization Approach

Опубликована: Янв. 2, 2024

Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study on the efficacy of Particle Swarm Optimization (PSO) combined with Histogram Equalization (HE) preprocessing segmentation, focusing Lung CT-Scan Chest X-ray datasets. Best Cost values reveal PSO algorithm’s performance, HE demonstrating significant stabilization enhanced convergence, particularly complex images. Evaluation metrics, including Accuracy, Precision, Recall, F-Score, Specificity, Dice, Jaccard, show substantial improvements preprocessing, emphasizing its impact accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, K-means, confirm competitiveness PSO-HE approach, especially The also underscores positive influence clarity precision. These findings highlight promise approach advancing accuracy reliability paving way further method integration to enhance this critical healthcare application.

Язык: Английский

---

Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario

Radiology Artificial Intelligence, Год журнала: 2024, Номер 6(4)

Опубликована: Июль 1, 2024

Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning. Materials Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001 through December 2015) from national consortium (n = 184; median age, 7 years [range, 1-23 years]; 94 male patients) cancer center 100; 8 1-19 47 to develop neural networks for low-grade glioma approach maximize performance in limited data scenario. The best was tested on an independent test set subjected randomized blinded evaluation by three clinicians, wherein they assessed expert- artificial intelligence (AI)-generated segmentations via 10-point Likert scales Turing tests. Results AI used in-domain (median Dice score coefficient, 0.88 [IQR, 0.72-0.91] vs 0.812 0.56-0.89] baseline model; P .049). With external testing, yielded excellent accuracy reference standards experts similarity coefficients: expert 1, 0.83 0.75-0.90]; 2, 0.81 0.70-0.89]; 3, 0.68-0.88]; mean accuracy, 0.82). For benchmarking 100 scans), rated AI-based higher average compared with other score, 9 7-9] [IQR 7-9]) more as clinically acceptable (80.2% 65.4%). Experts correctly predicted origin 26.0% cases. Conclusion Stepwise enabled expert-level automated autosegmentation volumetric measurement high level acceptability. Keywords: Transfer Learning, Pediatric Brain Tumors, Segmentation, Deep Learning Supplemental material is available article. © RSNA, 2024.

Язык: Английский

---

Coffee disease detection using a robust HSV color‐based segmentation and transfer learning for use on smartphones

International Journal of Intelligent Systems, Год журнала: 2021, Номер 37(8), С. 4967 - 4993

Опубликована: Ноя. 19, 2021

Ethiopia's coffee export accounts for about 34% of all exports the budget year 2019/2020. Making it 10th-largest exporter in world. Coffee diseases cause around 30% loss production annually. In this paper, we propose an approach detection four classes leaf diseases, Rust, Miner, Cercospora, and Phoma by using a fast Hue, Saturation, Value (HSV) color space segmentation MobileNetV2 architecture trained transfer learning. The proposed HSV algorithm constitutes separating from background infected spots on automatically finding best threshold value Saturation (S) channel space. was compared to YCgCr k-means algorithms, terms Mean Intersection Over Union F1-Score. outperformed these methods achieved MIoU score 72.13% F1 82.54%. also outperforms execution time, taking average 0.02 s per image diseased healthy spots. Our classifier 96% classification accuracy precision. faster make suitable deployment mobile devices as such has been successfully implemented smartphones running Android operating system.

Язык: Английский

---