Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 1, 2024

Abstract Skin cancer is a lethal disease, and its early detection plays pivotal role in preventing spread to other body organs tissues. Artificial Intelligence (AI)-based automated methods can play significant detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types skin cancers. The proposed method leverages network autoencoders, comprising two distinct autoencoders: spatial autoencoder FFT (Fast Fourier Transform)-autoencoder. spatial-autoencoder specializes learning features within input lesion images whereas FFT-autoencoder learns capture textural distinguishing frequency patterns transformed through reconstruction process. use attention modules at levels encoder part these autoencoders significantly improves their discriminative feature capabilities. An Extreme Learning Machine (ELM) with single layer feedforward trained classify malignancies using characteristics that were recovered from bottleneck layers autoencoders. 'HAM10000' 'ISIC-2017' are publicly available datasets used thoroughly assess suggested approach. experimental findings demonstrate accuracy robustness technique, AUC, precision, values dataset being 0.98, 97.68% 97.66%, 0.95, 86.75% 86.68%, respectively. highlights possibility approach accurate cancer.

Язык: Английский

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

Deleted Journal, Год журнала: 2024, Номер unknown

Опубликована: Окт. 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.

Язык: Английский

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

Опубликована: Янв. 1, 2025

Язык: Английский

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Advanced deep learning and large language models: Comprehensive insights for cancer detection

Image and Vision Computing, Год журнала: 2025, Номер unknown, С. 105495 - 105495

Опубликована: Март 1, 2025

Язык: Английский

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Deep Learning for Early Skin Cancer Detection: Combining Segmentation, Augmentation, and Transfer Learning

Big Data and Cognitive Computing, Год журнала: 2025, Номер 9(4), С. 97 - 97

Опубликована: Апрель 11, 2025

Skin cancer, particularly melanoma, is one of the leading causes cancer-related deaths. It essential to detect and start treatment in early stages for it be effective improve survival rates. This study developed evaluated a deep learning-based classification model classify skin lesion images as benign (non-cancerous) malignant (cancerous). In this study, we used ISIC 2016 dataset train segmentation Kaggle 10,000 model. We applied different data pre-processing techniques enhance robustness our generate binary mask with corresponding pre-processed image by overlaying its edges highlight region, before feeding transfer learning, using ResNet-50 backbone feedforward network. achieved an accuracy 92.80%, precision 98.64%, recall 86.80%. From have found that integrating learning proper improves model’s performance. Future work will focus on expanding datasets testing more architectures performance metrics

Язык: Английский

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Skin Cancer Detection Approach Using Convolutional Neural Network Artificial Intelligence

IJIIS International Journal of Informatics and Information Systems, Год журнала: 2024, Номер 7(2), С. 46 - 54

Опубликована: Март 31, 2024

Skin cancer is a type of that can cause death, where skin included in the 15 common cancers occur Indonesia. The number sufferers was around 6,170 cases non-melanoma and 1,392 melanoma 2018 Therefore, research related to classification increasing. This done as an initial step detecting whether lesion be said cancerous or not. deep learning approach has certainly shown promising results carrying out classification, so this proposes learning-based method used for classification. proposed involves Convolutional Neural Networks with ISIC 2017 dataset. models are InceptionV3, EfficientNetB0, ResNet50, MobileNetV2, NASNetMobile. highest accuracy single model produced reached 69.3% using MobileNetV2 model. An ensemble combining five also tested compared other result 80.6%.

Язык: Английский

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Automatic skin tumor detection in dermoscopic samples using Online Patch Fuzzy Region Based Segmentation

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 100, С. 107096 - 107096

Опубликована: Окт. 29, 2024

Язык: Английский

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Skin Cancer Classification Using DenseNet

Опубликована: Сен. 19, 2024

Язык: Английский

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Two-Stage Input-Space Image Augmentation and Interpretable Technique for Accurate and Explainable Skin Cancer Diagnosis

Computation, Год журнала: 2023, Номер 11(12), С. 246 - 246

Опубликована: Дек. 5, 2023

This research paper presents a deep-learning approach to early detection of skin cancer using image augmentation techniques. We introduce two-stage process utilizing geometric and generative adversarial network (GAN) differentiate categories. The public HAM10000 dataset was used test how well the proposed model worked. Various pre-trained convolutional neural (CNN) models, including Xception, Inceptionv3, Resnet152v2, EfficientnetB7, InceptionresnetV2, VGG19, were employed. Our demonstrates an accuracy 96.90%, precision 97.07%, recall 96.87%, F1-score 96.97%, surpassing performance other state-of-the-art methods. also discusses use Shapley Additive Explanations (SHAP), interpretable technique for diagnosis, which can help clinicians understand reasoning behind diagnosis improve trust in system. Overall, method promising automated that could patient outcomes reduce healthcare costs.

Язык: Английский

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Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

2020 International Seminar on Application for Technology of Information and Communication (iSemantic), Год журнала: 2023, Номер unknown, С. 485 - 489

Опубликована: Сен. 16, 2023

The field of dermatology faces considerable challenges when it comes to early detection skin cancer. Our study focused on using different datasets, including original data, augmented and SMOTE oversampled identify dataset consisted images lesions from the MNIST Skin Cancer (HAM 10000), samples both cancerous benign cases in dataset. We employed data augmentation expand dataset's size increase diversity lesion features. Furthermore, tackle class imbalance dataset, we applied oversampling technique generate synthetic for under-represented group. With original, augmented, trained a Convolutional Neural Network (CNN) model. performance model was evaluated accuracy, recall, precision, F1-score. comparison between results obtained clearly revealed distinctions performance. findings demonstrate that employing can significantly enhance efficacy cancer detection.

Язык: Английский

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