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

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

Prostate Cancer Classification Using Deep Learning Models

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 288 - 303

Published: April 26, 2024

A frequent cancer in male community is Prostate cancer. If it identified early stages, then will be curable. This diffusing all over the world including France, USA, Swedon and Ireland etc. More than 25,400 males are affected by this gland looks like walnut. Most of times grows slowly many men, unfortunately exponentially some people. It creates blood during urination semen. Early-stage identification needs close analysis complete diagnosis with medications. For purpose, deep learning methods suggested. In paper, convolution layer based model has been used. Out this, Visual Geometry Group-16 (VGG-16) yields accuracy 97.74% mobile net gives 86.24%. work suggests that cancers can treated kit models assisted software.

Language: Английский