Automatic Hybrid CNN-Based Skin Cancer Classification DOI
S. Singaravelan, Arun Shunmugam Dhiraviyam, Ajay Kumar

et al.

Advances in information security, privacy, and ethics book series, Journal Year: 2024, Volume and Issue: unknown, P. 23 - 58

Published: Dec. 27, 2024

Skin cancer, particularly dermo-cancer, is a critical health concern with rising incidences worldwide. Automated classification of dermo-cancer from skin images plays pivotal role in early diagnosis and timely intervention. In this work, hybrid architecture that integrates inception ResNet models to enhance feature extraction facilitate hierarchical learning for improved explored. The module contributes capturing multi-scale features, while the addresses challenges vanishing gradients aids building more robust deeper neural network. proposed trained on comprehensive dataset, experimental results demonstrate superior performance compared individual models, achieving enhanced accuracy, sensitivity, specificity. approach automated but also holds promise other medical image tasks, showcasing potential architectures analysis.

Language: Английский

A novel CNN-ViT-based deep learning model for early skin cancer diagnosis DOI
İshak Paçal, B. Özdemir, Javanshir Zeynalov

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107627 - 107627

Published: Jan. 28, 2025

Language: Английский

Citations

7
Enhanced Diagnosis of Skin Cancer from Dermoscopic Images Using Alignment Optimized Convolutional Neural Networks and Grey Wolf Optimization DOI Creative Commons
Faizan Mazhar, Naeem Aslam, Ahmad Naeem

et al.

Journal of Computing Theories and Applications, Journal Year: 2025, Volume and Issue: 2(3)

Published: Jan. 15, 2025

Skin cancer (SC) is a highly serious kind of that, if not addressed swiftly, might result in the patient’s demise. Early detection this condition allows for more effective therapy and prevents disease development. Deep Learning (DL) approaches may be used as an efficient tool SC (SCD). Several DL-based algorithms automated SCD have been reported. However, models are needed to improve accuracy. As result, paper introduces new strategy based on Grey Wolf optimization (GWO) methodologies CNN. The proposed methodology has four stages: preprocessing, segmentation, feature extraction, classification. method utilizes Convolutional Neural Network (CNN) extract features from Regions Interest (ROIs). CNN employed categorization, whereas GWO approach enhances accuracy by refining edge segmentation. This technique probabilistic model accelerate convergence algorithm. Employing optimize structure weight vectors CNNs can enhance diagnostic minimum 5%, evaluation outcomes. application its performance comparison with other methods indicate that predicted average 95.11% without Accuracy 92.66%, respectively, enhancing 2.5% when we train our GWO.

Language: Английский

Citations

1
Precision Lesion Analysis and Classification in Dermatological Imaging through Advanced Convolutional Architectures DOI Creative Commons

Shake Ibna Abir,

Shaharina Shoha,

Sarder Abdulla Al Shiam

et al.

Journal of Computer Science and Technology Studies, Journal Year: 2024, Volume and Issue: 6(5), P. 168 - 180

Published: Dec. 11, 2024

In this study, six convolutional neural network (CNN) architectures, VGG16, Inception-v3, ResNet, MobileNet, NasNet, and EfficientNet are tested on classifying dermatological lesions. The research preprocesses features extracts skin lesions data to achieve an accurate lesion classification in employing two benchmark datasets, HAM10000 ISIC-2019. CNN models then extract from the filtered, resized images (uniform dimensions: 128 × 3 pixels). These results show that consistently achieves higher accuracy, precision, recall, F1-score than any other model melanoma, basal cell carcinoma actinic keratoses, with 94.0%, 92.0%, 93.8%, respectively. competitive performance of NasNet is also demonstrated for eczema psoriasis. This study concludes proper preprocessing optimized architecture important image classification. promising, however, challenges such as imbalance datasets requirement larger ethically gathered exist. For future work, dataset diversity will be improved, along generalization, through interdisciplinary collaboration advanced architectures.

Language: Английский

Citations

4
Multi-residual attention network for skin lesion classification DOI
Haythem Ghazouani

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 103, P. 107449 - 107449

Published: Jan. 6, 2025

Language: Английский

Citations

0
Scalable COVID-19 classification using map reduce framework and deep learning enabled hunter Jaya African vultures optimization DOI

Bhagyashree R. Patle,

V. Vijayarajan

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 109943 - 109943

Published: Feb. 3, 2025

Language: Английский

Citations

0
Enhancing Skin Cancer Detection Through Category Representation and Fusion of Pre-Trained Models DOI

lingping kong,

Juan D. Velásquez, Václav Snåšel

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Enhancing skin lesion classification: a CNN approach with human baseline comparison DOI Creative Commons
Deep Ajabani, Zaffar Ahmed Shaikh, Amr Yousef

et al.

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2795 - e2795

Published: April 15, 2025

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high-confidence CNN while replacing low-confidence outputs expert assessments to enhance diagnostic accuracy. A model utilizing EfficientNetB3 backbone is trained datasets from ISIC-2019 and ISIC-2020 SIIM-ISIC melanoma classification challenges evaluated a 150-image test set. model’s are compared against 69 experienced medical professionals. Performance assessed using receiver operating characteristic (ROC) curves area under curve (AUC) metrics, alongside analysis resource costs. baseline achieves AUC 0.822, slightly below performance experts. However, improves true positive rate 0.782 reduces false 0.182, delivering better minimal involvement. offers scalable, resource-efficient solution address variability in image analysis, effectively harnessing complementary strengths humans CNNs.

Language: Английский

Citations

0
Skin Cancer Classifier: Performance Enhancement Using Deep Learning Models DOI
Swati Mishra, Megha Agarwal

2022 8th International Conference on Signal Processing and Communication (ICSC), Journal Year: 2025, Volume and Issue: unknown, P. 721 - 725

Published: Feb. 20, 2025

Language: Английский

Citations

0
Melanoma skin cancer detection and classification using cycle-consistent simplicial adversarial attention adaptation networks with banyan tree growth optimization in medical image processing DOI
Arvind Kumar Shukla,

Gaurav Agrawal,

Rabindra Prasad

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 108, P. 107914 - 107914

Published: April 29, 2025

Language: Английский

Citations

0
Metadata Enriched Multi-Instance Contrastive Learning for High-Quality Facial Skin Visual Representations DOI Creative Commons
Jihyo Kim, Sung‐Chul Kim,

Seungwon Seo

et al.

Applied Artificial Intelligence, Journal Year: 2025, Volume and Issue: 39(1)

Published: Feb. 14, 2025

Language: Английский

Citations

0