Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification DOI Creative Commons
Dip Kumar Saha,

Sana Rafi,

M. F. Mridha

et al.

BMC Infectious Diseases, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 25, 2025

The daily surge in cases many nations has made the growing number of human monkeypox (Mpox) an important global concern. Therefore, it is imperative to identify Mpox early prevent its spread. majority studies on identification have utilized deep learning (DL) models. However, research developing a reliable method for accurately detecting stages still lacking. This study proposes ensemble model composed three improved DL models more classify phases. We used widely recognized Skin Images Dataset (MSID), which includes 770 images. enhanced Swin Transformer (SwinViT), proposed Mpox-XDE, and modified models-Xception, DenseNet201, EfficientNetB7-were used. To generate model, were combined via Softmax layer, dense flattened 65% dropout. Four neurons final layer dataset into four categories: chickenpox, measles, normal, Mpox. Lastly, average pooling implemented actual class. Mpox-XDE performed exceptionally well, achieving testing accuracy, precision, recall, F1-score 98.70%, 98.90%, 98.80%, respectively. Finally, popular explainable artificial intelligence (XAI) technique, Gradient-weighted Class Activation Mapping (Grad-CAM), was applied convolutional overlaid areas that effectively highlight each illness class dataset. methodology will aid professionals diagnosing patient's condition.

Language: Английский

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

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(9), P. 27001 - 27035

Published: Aug. 22, 2023

Language: Английский

Citations

33
Attention to Monkeypox: An Interpretable Monkeypox Detection Technique Using Attention Mechanism DOI Creative Commons
Avi Deb Raha, Mrityunjoy Gain, Rameswar Debnath

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 51942 - 51965

Published: Jan. 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)

Language: Английский

Citations

16
An attention-based deep learning for acute lymphoblastic leukemia classification DOI Creative Commons
Malathy Jawahar,

L. Jani Anbarasi,

Sathiya Narayanan

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 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.

Language: Английский

Citations

10
Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review DOI Creative Commons
Malihe Ram, Mohammad Reza Afrash, Khadijeh Moulaei

et al.

BMC Cancer, Journal Year: 2024, Volume and Issue: 24(1)

Published: Aug. 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.

Language: Английский

Citations

10
Deep learning on medical image analysis DOI Creative Commons
Jiaji Wang, Shuihua Wang‎, Yudong Zhang

et al.

CAAI Transactions on Intelligence Technology, Journal Year: 2024, Volume and Issue: unknown

Published: June 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.

Language: Английский

Citations

9
DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification DOI
Umair Saeed,

Kamlesh Kumar,

Mansoor Ahmed Khuhro

et al.

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(7), P. 21019 - 21043

Published: July 25, 2023

Language: Английский

Citations

23
CoTCoNet: An optimized coupled transformer-convolutional network with an adaptive graph reconstruction for leukemia detection DOI
Chandravardhan Singh Raghaw, Arnav Sharma, Shubhi Bansal

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 179, P. 108821 - 108821

Published: July 6, 2024

Language: Английский

Citations

8
Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques DOI
Hari Mohan, Joon Yoo, Abdul Razaque

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124838 - 124838

Published: July 23, 2024

Language: Английский

Citations

8
A review of convolutional neural network based methods for medical image classification DOI

Chao Chen,

Nor Ashidi Mat Isa, Xin Liu

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 185, P. 109507 - 109507

Published: Dec. 3, 2024

Language: Английский

Citations

8
Attention guided grad-CAM : an improved explainable artificial intelligence model for infrared breast cancer detection DOI
Kaushik Raghavan,

B. Sivaselvan,

V. Kamakoti

et al.

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(19), P. 57551 - 57578

Published: Dec. 15, 2023

Language: Английский

Citations

15