Cancer detection and segmentation using machine learning and deep learning techniques: a review

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(9), С. 27001 - 27035

Опубликована: Авг. 22, 2023

Язык: Английский

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Attention to Monkeypox: An Interpretable Monkeypox Detection Technique Using Attention Mechanism

IEEE Access, Год журнала: 2024, Номер 12, С. 51942 - 51965

Опубликована: Янв. 1, 2024

In the wake of COVID-19, rising monkeypox cases pose a potential pandemic threat. While less severe than its increasing spread underscores urgency early detection and isolation to control disease. The main difficulty in diagnosing arises from prolonged diagnostic process symptoms that are similar those other skin diseases, making challenging. To address this, deployment deep learning models on edge devices presents viable solution for rapid accurate monkeypox. However, resource constraints require use lightweight models. limitation these often involves trade-off with accuracy, which is unacceptable context medical diagnostics. Therefore, development optimized both resource-efficient computing highly becomes imperative. this end, an attention-based MobileNetV2 model detection, capitalizing inherent design effective devices, proposed. This model, enhanced spatial channel attention mechanisms, tailored early-stage diagnosis better accuracy. We significantly improved Monkeypox Skin Images Dataset (MSID) by incorporating broader range classes thereby substantially enriching diversifying training dataset. helps distinguish particularly stages or when detailed examination unavailable. ensure transparency interpretability, we incorporated Gradient-weighted Class Activation Mapping (Grad-CAM) Local Interpretable Model-Agnostic Explanations (LIME) provide clear insights into model's reasoning. Finally, comprehensively assess performance our employed evaluation metrics, including Cohen's Kappa, Matthews Correlation Coefficient, Youden's J Index, alongside traditional measures like F1-score, precision, recall, sensitivity, specificity. demonstrated impressive results, outperforming baseline achieving 92.28% accuracy extended MSID dataset, 98.19% original 93.33% Lesion (MSLD)

Язык: Английский

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Deep learning on medical image analysis

CAAI Transactions on Intelligence Technology, Год журнала: 2024, Номер unknown

Опубликована: Июнь 24, 2024

Abstract Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring various diseases. Convolutional neural networks (CNNs) have become popular as they can extract intricate features patterns from extensive datasets. The paper covers the structure of CNN its advances explores different types transfer learning strategies well classic pre‐trained models. also discusses how has been applied to areas within medical analysis. This comprehensive overview aims assist researchers, clinicians, policymakers by providing detailed insights, helping them make informed decisions about future research policy initiatives improve patient outcomes.

Язык: Английский

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An attention-based deep learning for acute lymphoblastic leukemia classification

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 29, 2024

Abstract The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In United States, about 6500 occurrences of ALL are diagnosed each year both children and adults, comprising nearly 25% pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists reducing workload, providing correct results, managing enormous volumes data. Traditional CAD rely on hematologists’ expertise, specialized features, subject knowledge. Utilizing early detection can radiologists doctors making medical decisions. this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for classification blood cell images, focusing eosinophils, lymphocytes, monocytes, neutrophils. To tackle challenges like vanishing gradients enhance feature extraction, model incorporates Blocks (DRDB) faster convergence. Conventional residual blocks strategically placed between layers preserve original information extract general maps. Global Local Feature Enhancement (GLFEB) balance weak contributions from shallow improved normalization. global initial convolution layer, when combined with GLFEB-processed reinforces representations. Tanh function introduces non-linearity. A Channel Spatial Attention Block (CSAB) integrated into neural network emphasize or minimize specific channels, while fully connected transform use a sigmoid activation concentrates relevant features multiclass lymphoblastic leukemia was analyzed Kaggle dataset (16,249 images) categorized four classes, training testing ratio 80:20. Experimental results showed that DRDB, GLFEB CSAB blocks’ discrimination ability boosted DDRNet F1 score 0.96 minimal computational complexity optimum accuracy 99.86% 91.98% stands out existing methods due its high 91.98%, 0.96, complexity, enhanced ability. strategic combination these (DRDB, GLFEB, CSAB) designed address process, leading crucial accurate multi-class image identification. Their effective integration within contributes superior performance DDRNet.

Язык: Английский

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Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review

BMC Cancer, Год журнала: 2024, Номер 24(1)

Опубликована: Авг. 20, 2024

Navigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including need for accurate prediction disease progression response to treatment. Artificial intelligence (AI) presents a transformative approach that enables development sophisticated predictive models personalized treatment strategies enhance early detection improve therapeutic interventions better patient outcomes.

Язык: Английский

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DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(7), С. 21019 - 21043

Опубликована: Июль 25, 2023

Язык: Английский

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Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124838 - 124838

Опубликована: Июль 23, 2024

Язык: Английский

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A review of convolutional neural network based methods for medical image classification

Computers in Biology and Medicine, Год журнала: 2024, Номер 185, С. 109507 - 109507

Опубликована: Дек. 3, 2024

Язык: Английский

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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

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Machine learning-inspired intrusion detection system for IoT: Security issues and future challenges

Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110265 - 110265

Опубликована: Март 26, 2025

Язык: Английский

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