Detection Of Skin Cancer Using Deep Neural Networks

Rahi, Md. Muzahidul Islam; Khan, Farhan Tanvir; Mahtab, Mohammad Tanvir; Ullah, Ashraf; Alam, Md. Golam Rabiul; Alam, Md. Ashraful

doi:10.1109/csde48274.2019.9162400

Cited by 30 publications

(13 citation statements)

References 22 publications

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“…Table 1. Selected Keras networks models https://keras.io/api/applications/ In [30], several models of CNN neural network algorithms were used to determine their effectiveness in the diagnosis of several skin diseases and analysed their efficiency. Was used Keras Sequential API and transfer learning model includes VGG11, RESNET50, DENSENET121, achieve highest accuracy of 90%.…”

Section: Segmentation and Classification Methodsmentioning

confidence: 99%

“…Was used Keras Sequential API and transfer learning model includes VGG11, RESNET50, DENSENET121, achieve highest accuracy of 90%. In [30], several models of CNN neural network algorithms were used to determine their effectiveness in several skin diseases diagnosis and analysed their efficiency. The solution is based on VGGNet and the transfer learning paradigm, a sensitivity value of 78.66%, which is much higher than the state of the current technic state in this dataset.…”

Section: Segmentation and Classification Methodsmentioning

confidence: 99%

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Neural Networks From Keras in Skin Lesion Diagnostic

Michalska

2022

IAPGOS

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Abstract. Melanoma is currently one of the most dangerous skin diseases, in addition many others appear in the population. Scientists are developing techniques for early non-invasive skin lesions diagnosis from dermatoscopic images, for this purpose neural networks are increasingly used. Many tools are being developed to allow for faster implementation of the network, including the Keras package. . The article presents selected methods of diagnosing skin diseases, including the process of classification, features selection, extracting the skin lesion from the whole image.The described methods have been implemented using deep neural networks available in the Keras package. The article draws attention to the effectiveness, specificity, accuracy of classification based on available data sets, attention was paid to tools that allow for more effective operation of algorithms.

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Section: Segmentation and Classification Methodsmentioning

confidence: 99%

Section: Segmentation and Classification Methodsmentioning

confidence: 99%

Neural Networks From Keras in Skin Lesion Diagnostic

Michalska

2022

IAPGOS

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“…A dataset consists of around 2000 skin disease pictures collected from a standardized dataset Dermnet [69] containing 1800 training data pictures and 200 data test pictures, as shown in Tab. 2 and Fig.…”

Section: Datasetmentioning

confidence: 99%

Early Skin Disease Identification Using eep Neural Network

Gautam¹,

Trivedi²,

Anand³

et al. 2023

Computer Systems Science and Engineering

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Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists. Skin disease is the most common disorder triggered by fungus, viruses, bacteria, allergies, etc. Skin diseases are most dangerous and may be the cause of serious damage. Therefore, it requires to diagnose it at an earlier stage, but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy. This advance therapy involves financial burden and some other ill effects. Therefore, it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage. Several techniques have been proposed to detect skin disease at an earlier stage but fail to get accuracy. Therefore, the primary goal of this paper is to classify, detect and provide accurate information about skin diseases. This paper deals with the same issue by proposing a high-performance Convolution neural network (CNN) to classify and detect skin disease at an earlier stage. The complete methodology is explained in different folds: firstly, the skin diseases images are preprocessed with processing techniques, and secondly, the important feature of the skin images are extracted. Thirdly, the pre-processed images are analyzed at different stages using a Deep Convolution Neural Network (DCNN). The approach proposed in this paper is simple, fast, and shows accurate results up to 98% and used to detect six different disease types.

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“…Deep Learning capabilities that are more advanced the backend of the build process will use TensorFlow. At the same time, the frontend, Keras, will be utilized because of its high-level API features and user-friendliness [19][20][21][22].…”

Section: Keras Deep Learning Librarymentioning

confidence: 99%

Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)

Maduranga¹,

Nandasena²

2022

IJIGSP

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This paper presents a design and development of an Artificial Intelligence (AI) based mobile application to detect the type of skin disease. Skin diseases are a serious hazard to everyone throughout the world. However, it is difficult to make accurate skin diseases diagnosis. In this work, Deep learning algorithms Convolution Neural Networks (CNN) is proposed to classify skin diseases on the HAM10000 dataset. An extensive review of research articles on object identification methods and a comparison of their relative qualities were given to find a method that would work well for detecting skin diseases. The CNN-based technique was recognized as the best method for identifying skin diseases. A mobile application, on the other hand, is built for quick and accurate action. By looking at an image of the afflicted area at the beginning of a skin illness, it assists patients and dermatologists in determining the kind of disease present. Its resilience in detecting the impacted region considerably faster with nearly 2x fewer computations than the standard MobileNet model results in low computing efforts. This study revealed that MobileNet with transfer learning yielding an accuracy of about 85% is the most suitable model for automatic skin disease identification. According to these findings, the suggested approach can assist general practitioners in quickly and accurately diagnosing skin diseases using the smart phone.

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Detection Of Skin Cancer Using Deep Neural Networks

Cited by 30 publications

References 22 publications

Neural Networks From Keras in Skin Lesion Diagnostic

Neural Networks From Keras in Skin Lesion Diagnostic

Early Skin Disease Identification Using eep Neural Network

Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)

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