Skin Lesion Classification Using Additional Patient Information

Qilin, Sun; Huang, Chao; Chen, Minjie; Xu, Hanlin; Yang, Yali

doi:10.1155/2021/6673852

Cited by 20 publications

(9 citation statements)

References 15 publications

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“…The method achieved an accuracy of 59.3% for classifying the ISIC 2019 data set. Sun et al [ 13 ] applied a Grad-CAM method to generate a heat-map for ISIC 2018 image diagnostics along with metadata. Metadata contains patient data, lesion location, age, and sex.…”

Section: Related Workmentioning

confidence: 99%

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

et al. 2023

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Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients’ lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%.

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Section: Related Workmentioning

confidence: 99%

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

et al. 2023

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“…In another study, Sun et al proposed a single baseline for skin lesion classi cation which used the information of data augmentation as additional patient information. The metadata used in their manuscript included additional info that was generated during data augmentation [23]. Putra et al proposed a novel technique to perform dynamic pre-processing on the inference (DPI).…”

Section: Related Workmentioning

confidence: 99%

Diagnosing of Skin Lesions Using Deep Convolutional Neural Network and Support Vector Machines

Naghshbandi

Fathi

2022

Preprint

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The number of fatalities resulting from skin cancer has significantly increased over the past few years. Early diagnosis is highly important for the quick treatment of skin cancer. Computer-based dermoscopy analysis methods provide considerable information about the lesions that can be helpful to skin experts in the early detection of skin lesions. These computer-based diagnostic systems require image-processing algorithms to provide mathematical explanations of suspicious areas. Convolutional Neural Network (CNN) as one of the deep learning algorithms has high scalability in interaction with big data, and can automatically extract key image features for classification and segmentation of images. In this study, a hybrid model consisting of deep learning and machine learning method is proposed to classify different types of skin lesions. In this model, at first, an input image is pre-processed to remove the negative effect of Hairs on skin lesion detection and also to prepare it for applying to an efficient deep convolutional network employed as a feature extractor. Then Support Vector Machine (SVM) is utilized as a classifier to detect and classify different types of skin lesions.

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“…Limiting unnecessary medical references and prioritizing dermatology problems are additional benefits. A number of authors also spoke regarding the prospective advantages of automated diagnosis solutions leveraging deep learning (DL) based techniques [5] like CNNs and EfficientNet architecture, including increased certainty of diagnosis, decreased physician effort, and happier patients [6]. Their work outlines potential paths for subsequent studies, including the creation of sizable and varied datasets, the analysis of multimodal strategies, and the examination of explainable AI methods for categorization.…”

Section: Introductionmentioning

confidence: 99%

Pipelined Structure in the Classification of Skin Lesions Based on Alexnet CNN and SVM Model With Bi-Sectional Texture Features

Sathvika,

Anmisha,

Thanmayi

et al. 2024

IEEE Access

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The classification of skin lesions is crucial because it increases the likelihood that malignant skin lesions will be discovered early on, allowing for more effective treatment. Due to the abundance of lesion images and the possibility of human error, early detection can be difficult for dermatologists. This work aims to classify skin lesions using two pipelines that were designed using support vector machine (SVM) and AlexNet convolutional neural network (CNN) models. Pipeline-1 uses the AlexNet CNN, while pipeline-2 proposes a bisectional feature extraction approach with an SVM model. The skin lesion images are initially preprocessed and the lesion regions are segmented. The lesion regions are further subdivided into four regions based on the intensity mapping function. The bisectional features are then extracted from the subdivided regions and the extracted features are trained with the SVM model. The dataset used in the experiment is the HAM-10000 dataset and the PAD-UFES-20 dataset, which consists of dermatoscopic skin lesions images. Based on the models' accuracy, sensitivity, DCI, specificity, and F1-score, the experiment's findings will be assessed for five different skin lesion conditions. By accurately and effectively classifying skin lesions, the study's findings will help in the diagnosis and treatment of skin disorders. The SVM pipeline performs better than the AlexNet CNN pipeline where the SVM pipeline and AlexNet CNN pipeline result in an accuracy of 98.66% and 97.68% respectively for the HAM-10000 dataset. The AlexNet CNN and SVM pipeline structure results in an accuracy of 96.87% and 98.10% respectively for the PAD-UFES-20 dataset.

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Skin Lesion Classification Using Additional Patient Information

Cited by 20 publications

References 15 publications

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

Diagnosing of Skin Lesions Using Deep Convolutional Neural Network and Support Vector Machines

Pipelined Structure in the Classification of Skin Lesions Based on Alexnet CNN and SVM Model With Bi-Sectional Texture Features

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