ADVANCED SKIN CANCER DETECTION USING CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING DOI Open Access
Emrah Aslan, Yıldırım ÖZÜPAK

Middle East Journal of Science, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 18, 2024

This study investigates the effectiveness of MobileNetV2 transfer learning method and a deep based Convolutional Neural Network (CNN) model in categorization malignant benign skin lesions cancer diagnosis. Since is disease that can be cured with early detection but fatal if delayed, accurate diagnosis great importance. The was trained architecture performed classification task high accuracy on images lesions. Metrics such as accuracy, recall, precision F1 score obtained during training validation processes support performance model. 92.97%, Recall 92.71%, Precision 94.70% 93.47%. results show CNN-based reliable effective tool for diagnosis, small fluctuations phase require further data hyperparameter optimization to improve generalization ability demonstrates models enhanced offer powerful solution medical image problems have potential contribute development systems healthcare field.

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
A robust deep learning framework for multiclass skin cancer classification DOI Creative Commons
Burhanettin Özdemir, İshak Paçal

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 10, 2025

Skin cancer represents a significant global health concern, where early and precise diagnosis plays pivotal role in improving treatment efficacy patient survival rates. Nonetheless, the inherent visual similarities between benign malignant lesions pose substantial challenges to accurate classification. To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks separable self-attention mechanisms, tailored enhance feature extraction optimize classification performance. The inclusion of initial two stages is driven by their ability effectively capture fine-grained local features subtle patterns, which are critical for distinguishing visually similar lesion types. Meanwhile, adoption later allows selectively prioritize diagnostically relevant regions while minimizing computational complexity, addressing inefficiencies often associated with traditional mechanisms. was comprehensively trained validated on ISIC 2019 dataset, includes eight distinct skin categories. Advanced methodologies such as data augmentation transfer were employed further robustness reliability. proposed architecture achieved exceptional performance metrics, 93.48% accuracy, 93.24% precision, 90.70% recall, 91.82% F1-score, outperforming over ten Convolutional Neural Network (CNN) based Vision Transformer (ViT) models tested under comparable conditions. Despite its robust performance, maintains compact design only 21.92 million parameters, making it highly efficient suitable deployment. Proposed Model demonstrates accuracy generalizability across diverse classes, establishing reliable framework clinical practice.

Language: Английский

Citations

6
Quantum computational infusion in extreme learning machines for early multi-cancer detection DOI Creative Commons
Anas Bilal, Muhammad Shafiq, Waeal J. Obidallah

et al.

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 6, 2025

Language: Английский

Citations

5
An Innovative Deep Learning Framework for Skin Cancer Detection Employing ConvNeXtV2 and Focal Self-Attention Mechanisms DOI Creative Commons
B. Özdemir, İshak Paçal

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103692 - 103692

Published: Dec. 1, 2024

Language: Английский

Citations

10
Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets DOI Creative Commons
Omneya Attallah

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

Published: Sept. 4, 2024

Language: Английский

Citations

7
Can deep learning effectively diagnose cardiac amyloidosis with 99mTc-PYP scintigraphy? DOI
Adem Maman, İshak Paçal, Fatih Batı

et al.

Journal of Radioanalytical and Nuclear Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 19, 2024

Language: Английский

Citations

5
Comparison of deep transfer learning models for classification of cervical cancer from pap smear images DOI Creative Commons
Harmanpreet Kaur, Reecha Sharma,

Jagroop Kaur

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 31, 2025

Language: Английский

Citations

0
An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models DOI Creative Commons

J. D. Dorathi Jayaseeli,

J Briskilal,

C. Fancy

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 3, 2025

Skin cancer is the most dominant and critical method of cancer, which arises all over world. Its damaging effects can range from disfigurement to major medical expenditures even death if not analyzed preserved timely. Conventional models skin recognition require a complete physical examination by specialist, time-wasting in few cases. Computer-aided medicinal analytical methods have gained massive popularity due their efficiency effectiveness. This model assist dermatologists initial significant for early diagnosis. An automatic classification utilizing deep learning (DL) help doctors perceive kind lesion improve patient's health. The one hot topics research field, along with development DL structure. manuscript designs develops Detection Cancer Using an Ensemble Deep Learning Model Gray Wolf Optimization (DSC-EDLMGWO) method. proposed DSC-EDLMGWO relies on biomedical imaging. presented initially involves image preprocessing stage at two levels: contract enhancement using CLAHE noise removal wiener filter (WF) model. Furthermore, utilizes SE-DenseNet method, fusion squeeze-and-excitation (SE) module DenseNet extract features. For process, ensemble models, namely long short-term memory (LSTM) technique, extreme machine (ELM) model, stacked sparse denoising autoencoder (SSDA) employed. Finally, gray wolf optimization (GWO) optimally adjusts models' hyperparameter values, resulting more excellent performance. effectiveness approach evaluated benchmark database, outcomes measured across various performance metrics. experimental validation portrayed superior accuracy value 98.38% 98.17% under HAM10000 ISIC datasets other techniques.

Language: Английский

Citations

0
融合多尺度感受野与多级Hybrid Transformer遥感图像超分辨率重建 DOI

李博 Li Bo,

孔令云 Kong Lingyun,

赵明伟 Zhao Mingwei

et al.

Laser & Optoelectronics Progress, Journal Year: 2025, Volume and Issue: 62(6), P. 0628003 - 0628003

Published: Jan. 1, 2025

Citations

0
SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems DOI Creative Commons
Umesh Kumar Lilhore,

Yogesh Kumar Sharma,

Sarita Simaiya

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 28, 2025

Language: Английский

Citations

0