Deep learning: systematic review, models, challenges, and research directions

Neural Computing and Applications, Год журнала: 2023, Номер 35(31), С. 23103 - 23124

Опубликована: Сен. 7, 2023

Abstract The current development in deep learning is witnessing an exponential transition into automation applications. This can provide a promising framework for higher performance and lower complexity. ongoing undergoes several rapid changes, resulting the processing of data by studies, while it may lead to time-consuming costly models. Thus, address these challenges, studies have been conducted investigate techniques; however, they mostly focused on specific approaches, such as supervised learning. In addition, did not comprehensively other techniques, unsupervised reinforcement techniques. Moreover, majority neglect discuss some main methodologies learning, transfer federated online Therefore, motivated limitations existing this study summarizes techniques supervised, unsupervised, reinforcement, hybrid learning-based addition each category, brief description categories their models provided. Some critical topics namely, transfer, federated, models, are explored discussed detail. Finally, challenges future directions outlined wider outlooks researchers.

Язык: Английский

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A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations

Expert Systems with Applications, Год журнала: 2023, Номер 242, С. 122807 - 122807

Опубликована: Дек. 2, 2023

Deep learning has emerged as a powerful tool in various domains, revolutionising machine research. However, one persistent challenge is the scarcity of labelled training data, which hampers performance and generalisation deep models. To address this limitation, researchers have developed innovative methods to overcome data enhance model capabilities. Two prevalent techniques that gained significant attention are transfer self-supervised learning. Transfer leverages knowledge learned from pre-training on large-scale dataset, such ImageNet, applies it target task with limited data. This approach allows models benefit representations effectively new tasks, resulting improved generalisation. On other hand, focuses using pretext tasks do not require manual annotation, allowing them learn valuable large amounts unlabelled These can then be fine-tuned for downstream mitigating need extensive In recent years, found applications fields, including medical image processing, video recognition, natural language processing. approaches demonstrated remarkable achievements, enabling breakthroughs areas disease diagnosis, object understanding. while these offer numerous advantages, they also limitations. For example, may face domain mismatch issues between requires careful design ensure meaningful representations. review paper explores fields within past three years. It delves into advantages limitations each approach, assesses employing techniques, identifies potential directions future By providing comprehensive current methods, article offers guidance selecting best technique specific issue.

Язык: Английский

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Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview

IEEE Access, Год журнала: 2023, Номер 11, С. 12870 - 12886

Опубликована: Янв. 1, 2023

A tumor is carried on by rapid and uncontrolled cell growth in the brain. If it not treated initial phases, could prove fatal. Despite numerous significant efforts encouraging outcomes, accurate segmentation classification continue to be a challenge. Detection of brain tumors significantly complicated distinctions position, structure, proportions. The main disinterest this study stays offer investigators, comprehensive literature Magnetic Resonance (MR) imaging's ability identify tumors. Using computational intelligence statistical image processing techniques, research paper proposed several ways detect cancer This also shows an assessment matrix for specific system using particular systems dataset types. explains morphology tumors, accessible data sets, augmentation methods, component extraction, categorization among Deep Learning (DL), Transfer (TL), Machine (ML) models. Finally, our compiles all relevant material identification understanding including their benefits, drawbacks, advancements, upcoming trends.

Язык: Английский

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Vision Transformers, Ensemble Model, and Transfer Learning Leveraging Explainable AI for Brain Tumor Detection and Classification

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2023, Номер 28(3), С. 1261 - 1272

Опубликована: Апрель 12, 2023

The abnormal growth of malignant or nonmalignant tissues in the brain causes long-term damage to brain. Magnetic resonance imaging (MRI) is one most common methods detecting tumors. To determine whether a patient has tumor, MRI filters are physically examined by experts after they received. It possible for images different specialists produce inconsistent results since professionals formulate evaluations differently. Furthermore, merely identifying tumor not enough. begin treatment as soon possible, it equally important type has. In this paper, we consider multiclass classification tumors significant work been done on binary classification. order detect faster, more unbiased, and reliably, investigated performance several deep learning (DL) architectures including Visual Geometry Group 16 (VGG16), InceptionV3, VGG19, ResNet50, InceptionResNetV2, Xception. Following this, propose transfer learning(TL) based model called IVX16 three best-performing TL models. We use dataset consisting total 3264 images. Through extensive experiments, achieve peak accuracy $95.11\%$ , notation="LaTeX">$93.88\%$ notation="LaTeX">$94.19\%$ notation="LaTeX">$93.58\%$ notation="LaTeX">$94.5\%$ notation="LaTeX">$96.94\%$ VGG16, Xception, IVX16, respectively. Explainable AI evaluate validity each DL implement recently introduced Vison Transformer (ViT) models compare their obtained output with ensemble model.

Язык: Английский

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Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction

Pattern Recognition, Год журнала: 2024, Номер 153, С. 110553 - 110553

Опубликована: Май 6, 2024

Язык: Английский

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Improved Multiclass Brain Tumor Detection using Convolutional Neural Networks and Magnetic Resonance Imaging

International Journal of Advanced Computer Science and Applications, Год журнала: 2023, Номер 14(3)

Опубликована: Янв. 1, 2023

Recently, Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have been applied extensively for image recognition and classification tasks, with successful results in the field of medicine, such as medical analysis. Radiologists a hard time categorizing this lethal illness since brain tumors include variety tumor cells. Lately, methods based on computer-aided diagnostics claimed to employ magnetic resonance imaging help diagnosis cancers (MRI). (CNNs) are often used analysis, including detection cancers. This effort was motivated by difficulty that physicians appropriately detecting tumors, when they early stages bleeding. proposed model categorized into four distinct classes: (Normal, Glioma, Meningioma, Pituitary). The CNN networks reach 95% recall, 95.44% accuracy 95.36% F1-score.

Язык: Английский

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A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images

International Journal of Machine Learning and Cybernetics, Год журнала: 2024, Номер 15(9), С. 3579 - 3597

Опубликована: Март 5, 2024

Abstract Serious consequences due to brain tumors necessitate a timely and accurate diagnosis. However, obstacles such as suboptimal imaging quality, issues with data integrity, varying tumor types stages, potential errors in interpretation hinder the achievement of precise prompt diagnoses. The rapid identification plays pivotal role ensuring patient safety. Deep learning-based systems hold promise aiding radiologists make diagnoses swiftly accurately. In this study, we present an advanced deep learning approach based on Swin Transformer. proposed method introduces novel Hybrid Shifted Windows Multi-Head Self-Attention module (HSW-MSA) along rescaled model. This enhancement aims improve classification accuracy, reduce memory usage, simplify training complexity. Residual-based MLP (ResMLP) replaces traditional Transformer, thereby improving speed, parameter efficiency. We evaluate Proposed-Swin model publicly available MRI dataset four classes, using only test data. Model performance is enhanced through application transfer augmentation techniques for efficient robust training. achieves remarkable accuracy 99.92%, surpassing previous research models. underscores effectiveness Transformer HSW-MSA ResMLP improvements innovative diagnostic offering support diagnosis, ultimately outcomes reducing risks.

Язык: Английский

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A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends

IEEE Access, Год журнала: 2024, Номер 12, С. 41180 - 41218

Опубликована: Янв. 1, 2024

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and segmentation. There numerous types CNNs designed to meet specific needs requirements, including 1D, 2D, 3D CNNs, well dilated, grouped, attention, depthwise convolutions, NAS, among others. Each type CNN has its unique structure characteristics, making it suitable tasks. It's crucial gain thorough understanding perform comparative analysis these different understand their strengths weaknesses. Furthermore, studying the performance, limitations, practical applications each can aid in development new improved architectures future. We also dive into platforms frameworks that researchers utilize research or from perspectives. Additionally, we explore main fields like 6D vision, generative models, meta-learning. This survey paper provides comprehensive examination comparison architectures, highlighting architectural differences emphasizing respective advantages, disadvantages, applications, challenges, future trends.

Язык: Английский

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Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

Cluster Computing, Год журнала: 2024, Номер 27(8), С. 11187 - 11212

Опубликована: Май 20, 2024

Abstract The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination such conditions. Despite advancements Computer-Aided Diagnosis (CADx) systems powered by deep learning, challenge accurately classifying from MRI scans persists due to high variability tumor appearances subtlety early-stage manifestations. This work introduces novel adaptation EfficientNetv2 architecture, enhanced Global Attention Mechanism (GAM) Efficient Channel (ECA), aimed at overcoming these hurdles. enhancement not only amplifies model’s ability focus on salient features within complex images but also significantly improves classification accuracy tumors. Our approach distinguishes itself meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance detecting broad spectrum Demonstrated through extensive experiments large public dataset, our model achieves an exceptional high-test 99.76%, setting new benchmark MRI-based classification. Moreover, incorporation Grad-CAM visualization techniques sheds light decision-making process, offering transparent interpretable insights are invaluable clinical assessment. By addressing limitations inherent previous models, this study advances field medical imaging analysis highlights pivotal role enhancing interpretability learning models diagnosis. research sets stage advanced CADx systems, patient care outcomes.

Язык: Английский

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Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions

Computers in Biology and Medicine, Год журнала: 2024, Номер 175, С. 108412 - 108412

Опубликована: Апрель 16, 2024

Язык: Английский

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