Skin Cancer Classification with Deep Convolutional Neural Networks

Chen, Mingang; Chen, Wenjie; Chen, Wei; Cai, Lizhi; Chai, Gang

doi:10.1166/jmihi.2020.3078

Cited by 11 publications

(5 citation statements)

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“…The receiver operating characteristic curve (ROC-curve) corresponds to the performance of the proposed model at all classification thresholds. It is the graph of the true positive rate vs. false positive rate [ 36 , 37 ].

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Section: Methodsmentioning

confidence: 99%

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach

et al. 2022

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Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.

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…”

Section: Methodsmentioning

confidence: 99%

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach

et al. 2022

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“…In a similar vein, Mohapatra et al [ 27 ] utilized a MobileNet pretrained model on the HAM10000 dataset (7 classes) without modifications and achieved an accuracy of 80%. Moving to the N/A dataset with nine classes, Chen et al [ 28 ] achieved an accuracy of 83.74% using a ResNet50 pretrained model. The study emphasized the application of the ResNet50 model to achieve accuracy across nine classes of skin lesions.…”

Section: Related Workmentioning

confidence: 99%

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

Himel,

Islam,

Al-Aff

et al. 2024

International Journal of Biomedical Imaging

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

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“…Specifically, it is widely accepted in industries where takt time and accuracy are two important criteria. As ResNet is lighter than other deep learning-based classification models but a powerful classification learner, researchers and practitioners have long developed and employed it in their domain [10,11,14,15,[46][47][48][49][50][51][52].…”

Section: Resnet Frameworkmentioning

confidence: 99%

Application of YOLO and ResNet in Heat Staking Process Inspection

Jung

Rhee²

2022

Sustainability

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In the automobile manufacturing industry, inspecting the quality of heat staking points in a door trim involves significant labor, leading to human errors and increased costs. Artificial intelligence has provided the industry some aid, and studies have explored using deep learning models for object detection and image classification. However, their application to the heat staking process has been limited. This study applied an object detection algorithm, the You Only Look Once (YOLO) framework, and a classification algorithm, residual network (ResNet), to a real heat staking process image dataset. The study leverages the advantages of YOLO models and ResNet to increase the overall efficiency and accuracy of detecting heat staking points from door trim images and classify whether the detected heat staking points are defected or not. The proposed model achieved high accuracy in both object detection (mAP of 95.1%) and classification (F1-score of 98%). These results show that the developed deep learning models can be applied to the real-time inspection of the heat staking process. The models can increase productivity and quality while decreasing human labor cost, ultimately improving a firm’s competitiveness.

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Skin Cancer Classification with Deep Convolutional Neural Networks

Cited by 11 publications

References 0 publications

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach

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

Application of YOLO and ResNet in Heat Staking Process Inspection

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