A deep neural network using modified EfficientNet for skin cancer detection in dermoscopic images

Decision Analytics Journal, Год журнала: 2023, Номер 8, С. 100278 - 100278

Опубликована: Июнь 25, 2023

Artificial intelligence (AI) systems can assist in analyzing medical images and aiding the early detection of diseases. AI also ensure quality services by avoiding misdiagnosis caused human errors. This study proposes a deep neural network (DNN) model with fine-tuned training improved learning performance on dermoscopic for skin cancer detection. A knowledge base DL models is constructed combining different datasets. Transfer fine-tuning are implemented faster proposed limited dataset. The data augmentation techniques applied to enhance model. total 58,032 refined were used this study. output layered architecture aggregated perform binary classification cancer. trained investigated multiclass tasks. metrics confirm that DNN modified EfficientNetV2-M outperforms state-of-the-art learning-based models.

Язык: Английский

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MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selection

Diagnostics, Год журнала: 2023, Номер 13(19), С. 3063 - 3063

Опубликована: Сен. 26, 2023

Cancer is one of the leading significant causes illness and chronic disease worldwide. Skin cancer, particularly melanoma, becoming a severe health problem due to its rising prevalence. The considerable death rate linked with melanoma requires early detection receive immediate successful treatment. Lesion classification are more challenging many forms artifacts such as hairs, noise, irregularity lesion shape, color, irrelevant features, textures. In this work, we proposed deep-learning architecture for classifying multiclass skin cancer detection. consists four core steps: image preprocessing, feature extraction fusion, selection, classification. A novel contrast enhancement technique based on luminance information. After that, two pre-trained deep models, DarkNet-53 DensNet-201, modified in terms residual block at end trained through transfer learning. learning process, Genetic algorithm applied select hyperparameters. resultant features fused using two-step approach named serial-harmonic mean. This step increases accuracy correct classification, but some information also observed. Therefore, an developed best called marine predator optimization (MPA) controlled Reyni Entropy. selected finally classified machine classifiers final Two datasets, ISIC2018 ISIC2019, have been experimental process. On these obtained maximum 85.4% 98.80%, respectively. To prove effectiveness methods, detailed comparison conducted several recent techniques shows framework outperforms.

Язык: Английский

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SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

Mathematics, Год журнала: 2024, Номер 12(7), С. 1030 - 1030

Опубликована: Март 29, 2024

The medical sciences are facing a major problem with the auto-detection of disease due to fast growth in population density. Intelligent systems assist professionals early detection and also help provide consistent treatment that reduces mortality rate. Skin cancer is considered be deadliest most severe kind cancer. Medical utilize dermoscopy images make manual diagnosis skin This method labor-intensive time-consuming demands considerable level expertise. Automated methods necessary for occurrence hair air bubbles dermoscopic affects research aims classify eight different types cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), benign (BKs). In this study, we propose SNC_Net, which integrates features derived from through deep learning (DL) models handcrafted (HC) feature extraction aim improving performance classifier. A convolutional neural network (CNN) employed classification. Dermoscopy publicly accessible ISIC 2019 dataset utilized train validate model. proposed model compared four baseline models, EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), ResNet-101 (B4), six state-of-the-art (SOTA) classifiers. With an accuracy 97.81%, precision 98.31%, recall 97.89%, F1 score 98.10%, outperformed SOTA classifiers as well models. Moreover, Ablation study performed on its performance. therefore assists dermatologists other detection.

Язык: Английский

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DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

PLoS ONE, Год журнала: 2024, Номер 19(3), С. e0297667 - e0297667

Опубликована: Март 20, 2024

Skin cancer is a common affecting millions of people annually. cells inside the body that grow in unusual patterns are sign this invasive disease. The then spread to other organs and tissues through lymph nodes destroy them. Lifestyle changes increased solar exposure contribute rise incidence skin cancer. Early identification staging essential due high mortality rate associated with In study, we presented deep learning-based method named DVFNet for detection from dermoscopy images. To detect images pre-processed using anisotropic diffusion methods remove artifacts noise which enhances quality A combination VGG19 architecture Histogram Oriented Gradients (HOG) used research discriminative feature extraction. SMOTE Tomek resolve problem imbalanced multiple classes publicly available ISIC 2019 dataset. This study utilizes segmentation pinpoint areas significantly damaged cells. vector map created by combining features HOG VGG19. Multiclassification accomplished CNN maps. achieves an accuracy 98.32% on Analysis variance (ANOVA) statistical test validate model's accuracy. Healthcare experts utilize model at early clinical stage.

Язык: Английский

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Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System

International Journal of Biomedical Imaging, Год журнала: 2024, Номер 2024, С. 1 - 18

Опубликована: Фев. 3, 2024

Skin cancer is a significant health concern worldwide, and early accurate diagnosis plays crucial role in improving patient outcomes. In recent years, deep learning models have shown remarkable success various computer vision tasks, including image classification. this research study, we introduce an approach for skin classification using transformer, state-of-the-art architecture that has demonstrated exceptional performance diverse analysis tasks. The study utilizes the HAM10000 dataset; publicly available dataset comprising 10,015 lesion images classified into two categories: benign (6705 images) malignant (3310 images). This consists of high-resolution captured dermatoscopes carefully annotated by expert dermatologists. Preprocessing techniques, such as normalization augmentation, are applied to enhance robustness generalization model. transformer adapted task. model leverages self-attention mechanism capture intricate spatial dependencies long-range within images, enabling it effectively learn relevant features Segment Anything Model (SAM) employed segment cancerous areas from images; achieving IOU 96.01% Dice coefficient 98.14% then pretrained used architecture. Extensive experiments evaluations conducted assess our approach. results demonstrate superiority over traditional architectures general with some exceptions. Upon experimenting on six different models, ViT-Google, ViT-MAE, ViT-ResNet50, ViT-VAN, ViT-BEiT, ViT-DiT, found out ML achieves 96.15% accuracy Google’s ViT patch-32 low false negative ratio test dataset, showcasing its potential effective tool aiding dermatologists cancer.

Язык: Английский

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Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review

BMC Cancer, Год журнала: 2025, Номер 25(1)

Опубликована: Янв. 13, 2025

Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis crucial to improve patient outcomes. Dermoscopy, non-invasive imaging technique, aids in melanoma detection but can be limited by subjective interpretation. Recently, machine learning deep techniques have shown promise enhancing diagnostic precision automating the analysis of dermoscopy images. This systematic review examines recent advancements (ML) (DL) applications for prognosis using We conducted thorough search across multiple databases, ultimately reviewing 34 studies published between 2016 2024. The covers range model architectures, including DenseNet ResNet, discusses datasets, methodologies, evaluation metrics used validate performance. Our results highlight that certain such as DCNN demonstrated outstanding performance, achieving over 95% accuracy on HAM10000, ISIC other datasets from provides insights into strengths, limitations, future research directions methods prognosis. It emphasizes challenges related data diversity, interpretability, computational resource requirements. underscores potential transform through improved efficiency. Future should focus creating accessible, large interpretability increase clinical applicability. By addressing these areas, models could play central role advancing care.

Язык: Английский

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SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions

Cancers, Год журнала: 2023, Номер 15(14), С. 3604 - 3604

Опубликована: Июль 13, 2023

Skin cancer is a major public health concern around the world. identification critical for effective treatment and improved results. Deep learning models have shown considerable promise in assisting dermatologists skin diagnosis. This study proposes SBXception: shallower broader variant of Xception network. It uses as base model classification increases its performance by reducing depth expanding breadth architecture. We used HAM10000 dataset, which contains 10,015 dermatoscopic images lesions classified into seven categories, training testing proposed model. Using we fine-tuned new reached an accuracy 96.97% on holdout test set. SBXception also achieved significant enhancement with 54.27% fewer parameters reduced time compared to Our findings show that architecture can greatly improve categorization.

Язык: Английский

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Performance evaluation of E-VGG19 model: Enhancing real-time skin cancer detection and classification

Heliyon, Год журнала: 2024, Номер 10(10), С. e31488 - e31488

Опубликована: Май 1, 2024

Skin cancer is a pervasive and potentially life-threatening disease. Early detection plays crucial role in improving patient outcomes. Machine learning (ML) techniques, particularly when combined with pre-trained deep models, have shown promise enhancing the accuracy of skin detection. In this paper, we enhanced VGG19 model max pooling dense layer for prediction cancer. Moreover, also explored models such as Visual Geometry Group 19 (VGG19), Residual Network 152 version 2 (ResNet152v2), Inception-Residual (InceptionResNetV2), Dense Convolutional 201 (DenseNet201), 50 (ResNet50), Inception 3 (InceptionV3), For training, lesions dataset used malignant benign cases. The extract features divide into two categories: benign. are then fed machine methods, including Linear Support Vector (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR) our results demonstrate that combining E-VGG19 traditional classifiers significantly improves overall classification classification. compared performance baseline metrics (recall, F1 score, precision, sensitivity, accuracy). experiment provide valuable insights effectiveness various accurate efficient This research contributes to ongoing efforts create automated technologies detecting can help healthcare professionals individuals identify potential cases at an early stage, ultimately leading more timely effective treatments.

Язык: Английский

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An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

Symmetry, Год журнала: 2024, Номер 16(3), С. 366 - 366

Опубликована: Март 18, 2024

Skin cancer poses a serious risk to one’s health and can only be effectively treated with early detection. Early identification is critical since skin has higher fatality rate, it expands gradually different areas of the body. The rapid growth automated diagnosis frameworks led combination diverse machine learning, deep computer vision algorithms for detecting clinical samples atypical lesion specimens. Automated methods recognizing that use learning techniques are discussed in this article: convolutional neural networks, and, general, artificial networks. recognition symmetries key point dealing image datasets; hence, developing appropriate architecture as improve performance release capacities network. current study emphasizes need an method identify lesions reduce amount time effort required diagnostic process, well novel aspect using based on analysis concludes underlying research directions future, which will assist better addressing difficulties encountered human recognition. By highlighting drawbacks advantages prior techniques, authors hope establish standard future domain diagnostics.

Язык: Английский

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Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review

Diagnostics, Год журнала: 2024, Номер 14(4), С. 454 - 454

Опубликована: Фев. 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.

Язык: Английский

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