Diagnosing Skin Cancer Using Shearlet Transform Multiresolution Computation

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 26, 2024

Skin cancer diagnosis relies on the accurate analysis of medical images to identify malignant and benign lesions. The Shearlet transform, a powerful mathematical tool for multiresolution analysis, has shown promise in enhancing detection classification skin cancer. This study investigates application transform-based diagnosis. known its ability capture anisotropic features directional information, provides comprehensive representation lesion at multiple scales orientations. We integrate transform with advanced image processing techniques extract discriminative from dermoscopic images. These are then utilized train machine learning classifier, specifically support vector (SVM), distinguish between proposed methodology is evaluated publicly available dataset, results demonstrate significant improvements diagnostic accuracy compared traditional methods. Our approach enhances feature extraction capabilities, leading more reliable precise diagnosis, ultimately contributing better patient outcomes.

Language: Английский

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RvXmBlendNet: A Multi-architecture Hybrid Model for Improved Skin Cancer Detection

Human-Centric Intelligent Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 9, 2024

Abstract Skin cancer, one of the most dangerous cancers, poses a significant global threat. While early detection can substantially improve survival rates, traditional dermatologists often face challenges in accurate diagnosis, leading to delays treatment and avoidable fatalities. Deep learning models like CNN transfer have enhanced diagnosis from dermoscopic images, providing precise timely detection. However, despite progress made with hybrid models, many existing approaches still challenges, such as limited generalization across diverse datasets, vulnerability overfitting, difficulty capturing complex patterns. As result, there is growing need for more robust effective that integrate multiple architectures advanced mechanisms address these challenges. Therefore, this study aims introduce novel multi-architecture deep model called "RvXmBlendNet," which combines strengths four individual models: ResNet50 (R), VGG19 (v), Xception (X), MobileNet (m), followed by "BlendNet" signify their fusion into unified architecture. The integration achieved through synergistic combination architectures, incorporating self-attention using attention layers adaptive content blocks. This used HAM10000 dataset refine image preprocessing enhance accuracy. Techniques OpenCV-based hair removal, min–max scaling, histogram equalization were employed quality feature extraction. A comparative between proposed "RvXmBlendNet" (CNN, ResNet50, VGG19, Xception, MobileNet) demonstrated highest accuracy 98.26%, surpassing other models. These results suggest system facilitate earlier interventions, patient outcomes, potentially lower healthcare costs reducing invasive diagnostic procedures.

Language: Английский

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Early cancer detection using deep learning and medical imaging: A survey

Critical Reviews in Oncology/Hematology, Journal Year: 2024, Volume and Issue: 204, P. 104528 - 104528

Published: Oct. 15, 2024

Cancer, characterized by the uncontrolled division of abnormal cells that harm body tissues, necessitates early detection for effective treatment. Medical imaging is crucial identifying various cancers, yet its manual interpretation radiologists often subjective, labour-intensive, and time-consuming. Consequently, there a critical need an automated decision-making process to enhance cancer diagnosis. Previously, lot work was done on surveys different methods, most them were focused specific cancers limited techniques. This study presents comprehensive survey methods. It entails review 99 research articles collected from Web Science, IEEE, Scopus databases, published between 2020 2024. The scope encompasses 12 types cancer, including breast, cervical, ovarian, prostate, esophageal, liver, pancreatic, colon, lung, oral, brain, skin cancers. discusses techniques, medical data, image preprocessing, segmentation, feature extraction, deep learning transfer evaluation metrics. Eventually, we summarised datasets techniques with challenges limitations. Finally, provide future directions enhancing

Language: Английский

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Detection of Melanoma Insitu Using Trained CNN Model

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(8)

Published: Oct. 23, 2024

Language: Английский

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Transforming Skin Cancer Diagnosis: A Deep Learning Approach with the Ham10000 Dataset

Cancer Investigation, Journal Year: 2024, Volume and Issue: 42(10), P. 801 - 814

Published: Nov. 10, 2024

Skin cancer (SC) is one of the three most common cancers worldwide. Melanoma has deadliest potential to spread other parts body among all SCs. For SC treatments be effective, early detection essential. The high degree similarity between tumor and non-tumors makes diagnosis difficult even for experienced doctors. To address this issue, authors have developed a novel Deep Learning (DL) system capable automatically classifying skin lesions into seven groups: actinic keratosis (AKIEC), melanoma (MEL), benign (BKL), melanocytic Nevi (NV), basal cell carcinoma (BCC), dermatofibroma (DF), vascular (VASC) lesions. Authors introduced Multi-Grained Enhanced Cascaded Forest (Mg-EDCF) as DL model. In model, first, researchers utilized subsampled multigrained scanning (Mg-sc) acquire micro features. Second, employed two types Random (RF) create input Finally, (EDCF) was classification. HAM10000 dataset used implementing, training, evaluating proposed Transfer (TL) models such ResNet, AlexNet, VGG16. During validation training stages, performance four networks evaluated by comparing their accuracy loss. method outperformed competing with an average score 98.19%. Our methodology validated against existing state-of-the-art algorithms from recent publications, resulting in consistently greater accuracies than those classifiers.

Language: Английский

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