Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images

Lynn, Nay Chi; Kyu, Zin Mar

doi:10.1109/pdcat.2017.00028

Cited by 31 publications

(11 citation statements)

References 7 publications

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“…In [ 60 ], preliminary hair cleaning is performed using the DullRazor method, and the skin lesion image classification using a neural network classifier. The best result of recognition accuracy was 78.2%.…”

Section: Discussionmentioning

confidence: 99%

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

Lyakhov

Lyakhova

Nagornov

2022

Cancers

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Today, skin cancer is one of the most common malignant neoplasms in the human body. Diagnosis of pigmented lesions is challenging even for experienced dermatologists due to the wide range of morphological manifestations. Artificial intelligence technologies are capable of equaling and even surpassing the capabilities of a dermatologist in terms of efficiency. The main problem of implementing intellectual analysis systems is low accuracy. One of the possible ways to increase this indicator is using stages of preliminary processing of visual data and the use of heterogeneous data. The article proposes a multimodal neural network system for identifying pigmented skin lesions with a preliminary identification, and removing hair from dermatoscopic images. The novelty of the proposed system lies in the joint use of the stage of preliminary cleaning of hair structures and a multimodal neural network system for the analysis of heterogeneous data. The accuracy of pigmented skin lesions recognition in 10 diagnostically significant categories in the proposed system was 83.6%. The use of the proposed system by dermatologists as an auxiliary diagnostic method will minimize the impact of the human factor, assist in making medical decisions, and expand the possibilities of early detection of skin cancer.

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Section: Discussionmentioning

confidence: 99%

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

Lyakhov

Lyakhova

Nagornov

2022

Cancers

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“…There are a number of other works in which traditional machine learning methods like SVM [14,15], ANN [16,17] and decision trees [12,18] are utilized. However, with the advancement of deep learning, various possibilities for skin cancer detection have emerged.…”

Section: Related Workmentioning

confidence: 99%

Stacked Cross Validation with Deep Features

Al-Karawi

Avşar

2022

Teh. glas. (Online)

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Detection of malignant skin lesions is important for early and accurate diagnosis of skin cancer. In this work, a hybrid method for malignant lesion detection from dermoscopy images is proposed. The method combines the feature extraction process of convolutional neural networks (CNN) with an ensemble learner called stacked cross-validation (CV). The features extracted by three different CNN architectures, namely, ResNet50, Xception, and VGG16 are used for training of four different baseline classifiers, which are support vector machines, k-nearest neighbors, artificial neural networks, and random forests. The stacked outputs of these classifiers are used to train a logistic regression model as a meta-classifier. The performance of the proposed method is compared with the baseline classifiers trained individually as well as AdaBoost classifier, another ensemble learner. Feature extraction with Xception architecture, outperforms all other benchmark models by achieving scores of 0.909, 0.896, 0.886, and 0.917 for accuracy, F1-score, sensitivity, and AUC, respectively.

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“…Deep learning and Machine Learning algorithms are employed to look for patterns in the dataset that have not previously been identified. A classification model has been developed by some works by combining a vast number of different algorithms [21]. The preprocessed images had their features extracted using methods such as OTSU segmentation, and those extracted features were used in the classification process.…”

Section: Skin Cancermentioning

confidence: 99%

Recent Advancements in Early Diagnosis of Cancer: A Systematic Review

2022

IRJMETS

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Cancer is one of the most serious illnesses affecting people in this current generation. It is possible to prevent a lot of deaths by identifying the presence of this fatal disease at the earliest. Recent advancements in techniques for the development of web-based or mobile applications have facilitated the detection of multiple types of cancer. To diagnose cancer at its early stages, a variety of techniques and algorithms were applied to radiographs, histopathological images, MRI images, statistical data, and so on. This review will present the most recent research on the development of web-based and mobile applications for the early detection of cancers.

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Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images

Cited by 31 publications

References 7 publications

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

Stacked Cross Validation with Deep Features

Recent Advancements in Early Diagnosis of Cancer: A Systematic Review

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