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

Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images DOI Creative Commons
U. M. Prakash, S. Iniyan, Ashit Kumar Dutta

et al.

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

Published: Jan. 7, 2025

In the present scenario, cancerous tumours are common in humans due to major changes nearby environments. Skin cancer is a considerable disease detected among people. This uncontrolled evolution of atypical skin cells. It occurs when DNA injury cells, or genetic defect, leads an increase quickly and establishes malignant tumors. However, rare instances, many types occur from tempted by infrared light affecting worldwide health problem, so accurate appropriate diagnosis needed for efficient treatment. Current developments medical technology, like smart recognition analysis utilizing machine learning (ML) deep (DL) techniques, have transformed treatment these conditions. These approaches will be highly effective biomedical imaging. study develops Multi-scale Feature Fusion Deep Convolutional Neural Networks on Cancerous Tumor Detection Classification (MFFDCNN-CTDC) model. The main aim MFFDCNN-CTDC model detect classify using To eliminate unwanted noise, method initially utilizes sobel filter (SF) image preprocessing stage. For segmentation process, Unet3+ employed, providing precise localization tumour regions. Next, incorporates multi-scale feature fusion combining ResNet50 EfficientNet architectures, capitalizing their complementary strengths extraction varying depths scales input images. convolutional autoencoder (CAE) utilized classification method. Finally, parameter tuning process performed through hybrid fireworks whale optimization algorithm (FWWOA) enhance performance CAE A wide range experiments authorize approach. experimental validation approach exhibited superior accuracy value 98.78% 99.02% over existing techniques under ISIC 2017 HAM10000 datasets.

Language: Английский

Citations

2
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
Automated Skin Cancer Detection and Classification using Cat Swarm Optimization with a Deep Learning Model DOI Open Access

Vijay Arumugam Rajendran,

S. Saravanan

Engineering Technology & Applied Science Research, Journal Year: 2024, Volume and Issue: 14(1), P. 12734 - 12739

Published: Feb. 8, 2024

The application of Computer Vision (CV) and image processing in the medical sector is great significance, especially recognition skin cancer using dermoscopic images. Dermoscopy denotes a non-invasive imaging system that offers clear visuals cancers, allowing dermatologists to analyze identify various features crucial for lesion assessment. Over past few years, there has been an increasing fascination with Deep Learning (DL) applications recognition, particular focus on impressive results achieved by Neural Networks (DNNs). DL approaches, predominantly CNNs, have exhibited immense potential automating classification detection cancers. This study presents Automated Skin Cancer Detection Classification method Cat Swarm Optimization (ASCDC-CSODL). main objective ASCDC-CSODL enforce model recognize classify tumors In ASCDC-CSODL, Bilateral Filtering (BF) applied noise elimination U-Net employed segmentation process. Moreover, exploits MobileNet feature extraction Gated Recurrent Unit (GRU) approach used cancer. Finally, CSO algorithm alters hyperparameter values GRU. A wide-ranging simulation was performed evaluate performance model, demonstrating significantly improved over other approaches.

Language: Английский

Citations

14
Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review DOI Creative Commons
Pamela Hermosilla, Ricardo Soto, Emanuel Vega

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(4), P. 454 - 454

Published: Feb. 19, 2024

In recent years, there has been growing interest in the use of computer-assisted technology for early detection skin cancer through analysis dermatoscopic images. However, accuracy illustrated behind state-of-the-art approaches depends on several factors, such as quality images and interpretation results by medical experts. This systematic review aims to critically assess efficacy challenges this research field order explain usability limitations highlight potential future lines work scientific clinical community. study, was carried out over 45 contemporary studies extracted from databases Web Science Scopus. Several computer vision techniques related image video processing diagnosis were identified. context, focus process included algorithms employed, result accuracy, validation metrics. Thus, yielded significant advancements using deep learning machine algorithms. Lastly, establishes a foundation research, highlighting contributions opportunities improve effectiveness learning.

Language: Английский

Citations

9
Multi-Skin disease classification using hybrid deep learning model DOI Creative Commons

K. Jeyageetha,

K. Vijayalakshmi,

S. Suresh

et al.

Technology and Health Care, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 2, 2025

Among the many cancers that people face today, skin cancer is among deadliest and most dangerous. As a result, improving patients’ chances of survival requires to be identified classified early. Therefore, it critical assist radiologists in detecting through development Computer Aided Diagnosis (CAD) techniques. The diagnostic procedure currently makes heavy use Deep Learning (DL) techniques for disease identification. In addition, lesion extraction improved classification performance are achieved Region Growing (RG) based segmentation. At outset this study, noise reduced using an Adaptive Wiener Filter (AWF), hair removed Maximum Gradient Intensity (MGI). Then, best RG, which result integrating RG with Modified Honey Badger Optimiser (MHBO), does Finally, several forms DL model MobileSkinNetV2. experiments were conducted on ISIC dataset results show accuracy precision 99.01% 98.6%, respectively. comparison existing models, experimental proposed performs competitively, great news dermatologists treating cancer.

Language: Английский

Citations

1
Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition DOI Creative Commons
Yingzhe Yu,

Huiqiong Jia,

Li Zhang

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(3), P. 282 - 282

Published: March 12, 2025

The rising prevalence of skin lesions places a heavy burden on global health resources and necessitates an early precise diagnosis for successful treatment. diagnostic potential recent multi-modal lesion detection algorithms is limited because they ignore dynamic interactions information sharing across modalities at various feature scales. To address this, we propose deep learning framework, Multi-Modal Skin-Imaging-based Information-Switching Network (MDSIS-Net), end-to-end recognition. MDSIS-Net extracts intra-modality features using transfer in multi-scale fully shared convolutional neural network introduces innovative information-switching module. A cross-attention mechanism dynamically calibrates integrates to improve inter-modality associations representation this tested clinical disfiguring dermatosis data the public Derm7pt melanoma dataset. Visually Intelligent System Image Analysis (VISIA) captures five modalities: spots, red marks, ultraviolet (UV) porphyrins, brown spots dermatosis. model performs better than existing approaches with mAP 0.967, accuracy 0.960, precision 0.935, recall f1-score 0.947. Using dermoscopic pictures from dataset, outperforms current benchmarks melanoma, 0.877, 0.907, 0.911, 0.815, 0.851. model’s interpretability proven by Grad-CAM heatmaps correlating focus areas. In conclusion, our enhances identification capturing relationship fine-grained details images, improving both interpretability. This work advances decision making lays foundation future developments

Language: Английский

Citations

1
A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics DOI
Hari Mohan, Joon Yoo

Journal of Cancer Research and Clinical Oncology, Journal Year: 2023, Volume and Issue: 149(15), P. 14365 - 14408

Published: Aug. 4, 2023

Language: Английский

Citations

22
Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques DOI
Hari Mohan, Joon Yoo, Serhii Dashkevych

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 11, 2025

Language: Английский

Citations

0
Advanced deep learning and large language models: Comprehensive insights for cancer detection DOI
Yassine Habchi, Hamza Kheddar, Yassine Himeur

et al.

Image and Vision Computing, Journal Year: 2025, Volume and Issue: unknown, P. 105495 - 105495

Published: March 1, 2025

Language: Английский

Citations

0
MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features DOI

Nirupama,

Virupakshappa Virupakshappa

Deleted Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

The increasing prevalence of skin diseases necessitates accurate and efficient diagnostic tools. This research introduces a novel disease classification model leveraging advanced deep learning techniques. proposed architecture combines the MobileNet-V2 backbone, Squeeze-and-Excitation (SE) blocks, Atrous Spatial Pyramid Pooling (ASPP), Channel Attention Mechanism. was trained on four diverse datasets such as PH2 dataset, Skin Cancer MNIST: HAM10000 DermNet. ISIC dataset. Data preprocessing techniques, including image resizing, normalization, played crucial role in optimizing performance. In this paper, backbone is implemented to extract hierarchical features from preprocessed dermoscopic images. multi-scale contextual information fused by ASPP for generating feature map. attention mechanisms contributed significantly, enhancing extraction ability inter-channel relationships discriminative power features. Finally, output map converted into probability distribution through softmax function. outperformed several baseline models, traditional machine approaches, emphasizing its superiority with 98.6% overall accuracy. Its competitive performance state-of-the-art methods positions it valuable tool assisting dermatologists early classification. study also identified limitations suggested avenues future research, model's potential practical implementation field dermatology.

Language: Английский

Citations

3