Vision based Detection and Categorization of Skin lesions using Deep Learning Neural networks

Srividhya, V.; Sujatha, K.; Ponmagal, R.S.; Durgadevi, G.; Madheshwaran, L.

doi:10.1016/j.procs.2020.04.185

Cited by 33 publications

(9 citation statements)

References 6 publications

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“…These features are used as a reference for learning convolutional neural networks [20]. Melanoma is a deadly type of skin cancer [21], [22]. So, we need a computer-based system that has a good learning algorithm.…”

Section: Research Methods 21 Related Workmentioning

confidence: 99%

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A convolutional neural network for skin cancer classification

Nafi’iyah

Yuniarti

2022

IJ-ICT

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<span>Skin diseases can be seen clearly by oneself and others. Although this disease is visible on the skin, sometimes we worry if this skin disease is not mild. Some people experience skin diseases directly and quickly go to a dermatologist to have their complaints and symptoms checked. This skin protects the body, especially from the sun, so it can lead to death if something goes wrong. One example of a skin disease that can be deadly is skin cancer or skin tumors. In this research, we classified skin cancer into Benign and Malignant using the convolution neural network (CNN) algorithm. The purpose of this research is to develop the CNN architecture to help identify skin diseases. We used a dataset of 3,297 skin cancer images which are publicly available on the Kaggle website. We propose two CNN architectures that differ in the number of parameters. The first architecture has 6,427,745 parameters, and the second architecture has 2,797,665. With both architectures, the accuracy of the first model is 93%, and the second model is 74%. The first model with the number of parameters 6,427,745 We save for use in the creation of the website. We created a web-based application with the Django framework for skin disease identification.</span>

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Section: Research Methods 21 Related Workmentioning

confidence: 99%

“…One good learning algorithm is a convolutional neural network (CNN). CNN can be used to identify malignant tumors on the skin surface with a sensitivity value of 93.3% [22]. This research develops the CNN architecture to identify skin diseases.…”

Section: Research Methods 21 Related Workmentioning

confidence: 99%

A convolutional neural network for skin cancer classification

Nafi’iyah

Yuniarti

2022

IJ-ICT

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“…Additionally, many hybrid techniques integrate CNN with different classifiers to enhance melanoma detection accuracy. A structured scheme for analyzing and assessing the possibilities of melanoma is proposed by Srividhya [35].…”

Section: Hybrid Techniquesmentioning

confidence: 99%

An Aseptic Approach Towards Skin Lesion Localization and Grading using Deep Learning and Harris Hawks Optimization

Balaha

Hassan

El-Gendy

et al. 2022

Preprint

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Skin cancer is the most common form of cancer. Hence, the lives of millions of people are affected by this cancer every year. Approximately, it is predicted that the total number of cases of cancer will double in the next fifty years. It is an expensive procedure to discover skin cancer types in the early stages. Additionally, the survival rate reduces as cancer progresses. The current study proposes an aseptic approach toward skin lesion detection, classification, and segmentation using deep learning and a meta-heuristic optimizer called Harris Hawks Optimization Algorithm (HHO). The current study utilized the manual and automatic segmentation approaches. The manual segmentation is used when the dataset has no masks to use while the automatic segmentation approach is used, using U-Net models, to build an adaptive segmentation model. Additionally, the meta-heuristic HHO optimizer is utilized to achieve the optimization of the hyperparameters using 5 pre-trained CNN models (i.e., VGG16, VGG19, DenseNet169, DenseNet201, and MobileNet). Two are collected (i.e., "Melanoma Skin Cancer Dataset of 10000 Images" and "Skin Cancer ISIC" dataset) from two publically available sources. For the segmentation, the best-reported scores are 0.15908, 91.95%, 0.08864, 0.04313, 0.02072, 0.20767 in terms of loss, accuracy, Mean Absolute Error, Mean Squared Error, Mean Squared Logarithmic Error, and Root Mean Squared Error, respectively. For the "Melanoma Skin Cancer Dataset of 10000 Images" dataset, from the applied experiments, the best reported overall accuracy is 97.08% by the DenseNet169 pre-trained model. For the "Skin Cancer ISIC" dataset, the best reported overall accuracy is 96.06% by the MobileNet pre-trained model. After computing the results, the suggested approach is compared with 9 related studies.

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“…The primary concern is data; in natural language processing, it is the corpus [ 25 ]. Second, corpus must be carefully screened in terms of quality and data scale to ensure that the results generated have sufficient accuracy [ 26 ].…”

Section: Theoretical Analysis and Research Designmentioning

confidence: 99%

Couplet Analysis of Linguistic Topology Using Deep Neural Networks in Cognitive Linguistics

Zhu

Wang

Fang

2022

Computational Intelligence and Neuroscience

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The work reported here primarily aims to realize the automatic generation of couplets using the linguistic topology of deep neural network (DNN). First, the symmetry, topology, and cognitive linguistics of language are explored to lay a theoretical foundation for subsequent model establishment and analysis. Then, the recurrent neural network (RNN) is employed to build the Seq2Seq model, and Liweng’s Guide to Rhyme (an ancient Chinese enlightenment reading material to poetry creation) is imported into the Seq2Seq model as a basic corpus. Eventually, the entire system is implemented automatically on TensorFlow. The system undergoes tests of the five-character quatrain, the seven-character quatrain, the couplet, and the part-of-speech detection. Results demonstrate that both the first and the second lines of the couplet present an excellent correspondence regarding sentences and words. After some famous verses are entered, the second line of the couplet obtained is quite vivid and appropriate. Meanwhile, the results can be generated quickly and meet the requirements on rhyme and couplet matching. This model can input verses according to users’ own needs and generate the second line of the couplet quickly, showing good correspondence in words, part-of-speech, and sentence pattern. Because the couplet belongs to Chinese traditional culture, it has a strong local Chinese cultural flavor. The system designed based on computer technology can help people learn and experience the charm of couplets.

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Vision based Detection and Categorization of Skin lesions using Deep Learning Neural networks

Cited by 33 publications

References 6 publications

A convolutional neural network for skin cancer classification

A convolutional neural network for skin cancer classification

An Aseptic Approach Towards Skin Lesion Localization and Grading using Deep Learning and Harris Hawks Optimization

Couplet Analysis of Linguistic Topology Using Deep Neural Networks in Cognitive Linguistics

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