An Interpretable Skin Cancer Classification Using Optimized Convolutional Neural Network for a Smart Healthcare System

IEEE Access, Год журнала: 2023, Номер 11, С. 41003 - 41018

Опубликована: Янв. 1, 2023

Skin cancer is a prevalent form of malignancy globally, and its early accurate diagnosis critical for patient survival. Clinical evaluation skin lesions essential, but it faces challenges such as long waiting times subjective interpretations. Deep learning techniques have been developed to tackle these assist dermatologists in making more diagnoses. Prompt treatment vital prevent progression potentially life-threatening consequences. The use deep algorithms can improve the speed accuracy diagnosis, leading earlier detection treatment. Additionally, reduce workload healthcare professionals, allowing them concentrate on complex cases. goal this study was develop reliable (DL) prediction models classification; (i) deal with typical severe class imbalance problem, which arises because skin-affected patients' significantly smaller than healthy class; (ii) interpret model output better understand decision-making mechanism (iii) Propose an End-to-End smart system through android application. In comparison examination six well-known classifiers, effectiveness proposed DL technique explored terms metrics relating both generalization capability classification accuracy. A used HAM10000 dataset optimized CNN identify seven forms cancer. trained using two optimization functions (Adam RMSprop) three activation (Relu, Swish, Tanh). Furthermore, XAI-based lesion developed, incorporating Grad-CAM Grad-CAM++ explain model's decisions. This help doctors make informed diagnoses their stages, 82% 0.47% loss

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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LBO-MPAM: Ladybug Beetle Optimization-based multilayer perceptron attention module for segmenting the skin lesion and automatic localization

Journal of Experimental & Theoretical Artificial Intelligence, Год журнала: 2024, Номер unknown, С. 1 - 26

Опубликована: Янв. 21, 2024

In recent years, skin cancer has been the most dangerous disease noticed among people worldwide. Skin should be identified earlier to reduce rate of mortality. Employing dermoscopic images can identify and categorise effectively. But, visual evaluation is a complex procedure done in image. However, Deep learning (DL) an efficient method for detection; however, segmenting lesion automatic localisation stage complicated. this paper, novel Ladybug Beetle Optimization-Double Attention Based Multilevel 1-D CNN (LBO-DAM CNN) technique proposed detect classify cancer. To improve type discriminability, two types attention modules are introduced. The Ultra-Lightweight Subspace Module (ULSAM) utilised classifying feature maps into different stages validate frequency from image samples. multilayer perceptron module (MLPAM) determined provide information regarding classification diminish noise unwanted data. minimise data loss, it then combined with hierarchical complementarity during classification. Second, modified MLPAM used extract significant spaces network learning, select important information, space redundancy. Optimization (LBO) algorithm provides optimal solution by minimising loss DAM architecture. experimentation conducted on three datasets such as ISIC2020, HAM10000, melanoma detection dataset. experimental results revealed that compared existing methods IMFO-KELM, Mask RCNN, M-SVM, DCNN-9, TL-CNN datasets. These attained 94.56, 92.65, 90.56, 88.65, 95.5 ISIC2020 dataset but enhanced performance attaining 97.02. Also, validation based metrics, namely, accuracy, precision, sensitivity, F1-score 97.03%, 97.05%, 97.58%, 97.27% total 500 epochs.

Язык: Английский

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Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Diagnostics, Год журнала: 2023, Номер 13(19), С. 3147 - 3147

Опубликована: Окт. 7, 2023

Skin lesions are essential for the early detection and management of a number dermatological disorders. Learning-based methods skin lesion analysis have drawn much attention lately because improvements in computer vision machine learning techniques. A review most-recent classification, segmentation, is presented this survey paper. The significance healthcare difficulties physical inspection discussed state-of-the-art papers targeting classification then covered depth with goal correctly identifying type from dermoscopic, macroscopic, other image formats. contribution limitations various techniques used selected study papers, including deep architectures conventional methods, examined. looks into focused on segmentation that aimed to identify precise borders classify them accordingly. These make it easier conduct subsequent analyses allow measurements quantitative evaluations. paper discusses well-known algorithms, deep-learning-based, graph-based, region-based ones. difficulties, datasets, evaluation metrics particular also discussed. Throughout survey, notable benchmark challenges, relevant highlighted, providing comprehensive overview field. concludes summary major trends, potential future directions detection, aiming inspire further advancements critical domain research.

Язык: Английский

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DM-CNN: Dynamic Multi-scale Convolutional Neural Network with uncertainty quantification for medical image classification

Computers in Biology and Medicine, Год журнала: 2023, Номер 168, С. 107758 - 107758

Опубликована: Ноя. 29, 2023

Язык: Английский

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Performance Enhancement of Skin Cancer Classification Using Computer Vision

IEEE Access, Год журнала: 2023, Номер 11, С. 72120 - 72133

Опубликована: Янв. 1, 2023

Nowadays, computer vision plays an essential role in disease detection, computer-aided diagnosis, and patient risk identification. This is especially true for skin cancer, which can be fatal if not diagnosed its early stages. For this purpose, several diagnostic detection systems have been created the past. They were limited their performance because of complicated visual characteristics lesion images, included inhomogeneous features hazy borders. In paper, we proposed two methods detecting classifying dermoscopic images into benign malignant tumors. The first method using k-nearest neighbor (KNN) as classifier when pretrained deep neural networks are used feature extractors. second one AlexNet with grey wolf optimizer, that optimizes AlexNet's hyperparameters to get best results. We also tested approaches cancer machine learning (ML) (DL). ML approach artificial network, KNN, support vector machine, Naive Bayes, decision tree. DL contains convolutional network networks: AlexNet, VGG-16, VGG-19, EfficientNet-b0, ResNet-18, ResNet-50, ResNet-101, DenseNet-201, Inception-v3, MobileNet-v2. Our experiments trained on 4000 from ISIC archive dataset. outcomes showed outperformed other approaches. Accuracy exceeded 99% some models achieved 99%.

Язык: Английский

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D2LFS2Net: Multi‐class skin lesion diagnosis using deep learning and variance‐controlled Marine Predator optimisation: An application for precision medicine

CAAI Transactions on Intelligence Technology, Год журнала: 2023, Номер unknown

Опубликована: Авг. 30, 2023

Abstract In computer vision applications like surveillance and remote sensing, to mention a few, deep learning has had considerable success. Medical imaging still faces number of difficulties, including intra‐class similarity, scarcity training data, poor contrast skin lesions, notably in the case cancer. An optimisation‐aided learning‐based system is proposed for accurate multi‐class lesion identification. The sequential procedures start with preprocessing end categorisation. step where hybrid enhancement technique initially identification healthy regions. Instead flipping rotating outputs from middle phases enhanced are employed data augmentation next step. Next, two pre‐trained models, MobileNetV2 NasNet Mobile, trained using transfer on upgraded enriched dataset. Later, dual‐threshold serial approach obtain combine features both models. was variance‐controlled Marine Predator methodology, which authors as superior optimisation method. top fused feature vector classified machine classifiers. experimental strategy provided accuracy 94.4% publicly available dataset HAM10000. Additionally, framework evaluated compared current approaches, remarkable results.

Язык: Английский

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A systematic literature survey on skin disease detection and classification using machine learning and deep learning

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(32), С. 78093 - 78124

Опубликована: Фев. 26, 2024

Язык: Английский

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MOX-NET: Multi-stage deep hybrid feature fusion and selection framework for monkeypox classification

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124584 - 124584

Опубликована: Июнь 26, 2024

Язык: Английский

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FDUM-Net: An enhanced FPN and U-Net architecture for skin lesion segmentation

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 91, С. 106037 - 106037

Опубликована: Фев. 7, 2024

Язык: Английский

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