Preprocessing of Skin Cancer Using Anisotropic Diffusion and Sigmoid Function

Sau, Kartik; Maiti, Ananjan; Ghosh, Anay

doi:10.1007/978-981-10-8237-5_6

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…These works are participated in task‐1 of ISIC‐2017 challenges, the drawbacks of these approaches are presented in the literature. The proposed method also compared with the non‐participated and published work of skin cancer segmentation methods such as PSPNet (Pacheco et al, 2019), DTL (Rahmat et al, 2016) and U‐Net (Sau et al, 2018). Compared to all these works, the proposed FrCN segmentation obtained the best segmentation results because initially hair and noise is removed from the skin lesions.…”

Section: Resultsmentioning

confidence: 99%

“…This method was suffered with the improper classification and segmentation issues due to inaccurate and improper hair removal operation. In (Sau et al, 2018) authors used the contrast adjustment, normalization, anisotropic diffusion filter (ADF) along with calculation of gradient thresholding parameter through sigmoid function. The method was unable to remove the hair content due to over enhancements.…”

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confidence: 99%

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SLDCNet: Skin lesion detection and classification using full resolution convolutional network‐based deep learning CNN with transfer learning

Varma¹,

Paturu

Mishra³

et al. 2022

Expert Systems

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Background Skin cancer is one of the life threating diseases in the world. So, millions of lives can be saved by early detection of skin cancer. In addition, automating the computer‐aided system of skin lesion detection and classification (SLDC) will assist the medical practitioners to ensure more efficacious treatment of skin lesion disease. Material and Method In this article, a hybrid preprocessing‐based transfer learning model for SLDC is proposed, which is named as SLDCNet. Initially, the hybrid Gaussian filter (HGF) with connected component label (CCL) based fast march inpainting procedure is used for hair removal and denoising of skin lesions. Next, full resolution convolutional networks (FrCN) based segmentation method is adapted for detecting the cancer region. Then, feature extraction is performed using deep residual learning and finally, transfer learning mechanism is applied for classification of eight skin lesions. Results The extensive simulation results shows that proposed SLDCNet resulted in a classification accuracy of 99.92%, sensitivity of 99%, and specificity of 99.36%, respectively. Conclusion From the obtained results, it is proven that proposed SLDCNet provides better performance as compared to state‐of‐art SLDC approaches, and even the standard ISIC‐2019 public challenge.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

SLDCNet: Skin lesion detection and classification using full resolution convolutional network‐based deep learning CNN with transfer learning

Varma¹,

Paturu

Mishra³

et al. 2022

Expert Systems

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show abstract

“…Authors developed a unique meta-heuristic method called the DHOA-NN algorithm in reference to the work they did in [17]. The minute movements made by the buck are quickly detected, and the visual capacity of a buck is five times greater than that of a person, both of which contribute to the difficulty of the hunting process [18].…”

Section: Literature Surveymentioning

confidence: 99%

SCDC-Net: Detection and Classification of Skin Cancer using Deep q Neural Network with Hybrid Features

M. Kavitha

2024

jes

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The increment in radiation in atmosphere causing wide spread of skin cancer disease around the globe. Cancer of the skin is one of the most lethal forms of the illness, and it is responsible for millions of deaths annually all over the world. Because of this, the early identification of skin cancer using computer-aided diagnostic (CAD) technology might potentially save a person's life. However, skin cancer classification of performance is affected by hair artifacts and improper segmentation of lesion. The conventional methods were failed to achieve higher performance due to improper feature analysis. Therefore, to achieve the robust performance, this work was developed the transfer learning-based skin cancer detection and classification (SCDC-Net). Initially, UG-Net model was introduced to eliminate hair from skin lesions by enhancing the region of interest, which is developed by combining U-shaped deep learning network (U-Net) with generative adversarial networks (GAN). In addition, Hybrid U-Net (HU-Net) perform the segmentation operation for identifying the disease effected region of interest. Further, the features are extracted using gray level cooccurrence matrix (GLCM) matrix and discrete wavelet transform (DWT), which extracts the color, texture, statistical features. Finally, these combined characteristics are used to train a deep q neural network (DQNN) model, which then classifies a number of different skin cancer illness categories. The simulations conducted on real-time ISIC-2019 dataset proved that proposed SCDC-Net outperformed in all performance measures as compared to existing methods.

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“…In this case, quality of the rebuilt lesion is improved due to the removal of hair, thus the system records the best performance. [11], CUOI [13], HI [14], FGF-CEF [15], BHF [16], ADF [17], and TL-CNN [18].…”

Section: Performance Hair Removal and Filtering Approachesmentioning

confidence: 99%

“…As a result of the inadequacies in hair removal, the categorization and segmentation of this procedure were faulty. Gradient Thresholding Parameter was calculated using an anisotropic diffusion filter (ADF) with normalization and contrast adjustment properties, as well as a sigmoid function, as described in [17]. Because to over-enhancements, the method was unable to completely eradicate hair.…”

Section: Introductionmentioning

confidence: 99%

SCDC-Net: Skin Cancer Detection and Classification using Deep Q Neural Network with Hybrid Features

Nyemeesha

2022

IJRITCC

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Skin cancer is one of the most severe malignancies that may affect individuals of all ages. It can affect people of all races and ethnicities. Thus, early detection and treatment of skin cancer have the potential to save millions of lives. Because of the high degree of similarity between skin lesions, conventional machine learning approaches failed to provide the highest possible detection and classification accuracy in the skin cancer detection and classification (SCDC) system. To achieve the robust performance, this work is presented a SCDC-Net by employing deep learning, transfer learning models with hybrid features. Initially, UG-Net based transfer learning model is developed by combining generative adversarial networks (GAN) and U-Net. Here, UG-Net is used to remove hair from skin lesions and also preprocess enhances lesion. In addition, Hybrid U-Net (HU-Net) is used to localize the disease effected area of skin cancer. Further, various hybrid features are extracted from the segmented output by employing the gray-level cooccurrence matrix (GLCM) based texture characteristics, the discrete wavelet transform (DWT)-based low-level features, and the computation of image statistics for the texture, low-level, and colour features, respectively. In order to ensure the highest possible performance of the system, a deep q neural network (DQNN) was constructed for the classification of skin cancer using GLCM, DWT, and statistical colour features. The simulation results shows that the proposed method resulted in superior subjective and objective performance as compared to state of art approaches on ISIC-2019 public challenge dataset.

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Preprocessing of Skin Cancer Using Anisotropic Diffusion and Sigmoid Function

Cited by 10 publications

References 10 publications

SLDCNet: Skin lesion detection and classification using full resolution convolutional network‐based deep learning CNN with transfer learning

SLDCNet: Skin lesion detection and classification using full resolution convolutional network‐based deep learning CNN with transfer learning

SCDC-Net: Detection and Classification of Skin Cancer using Deep q Neural Network with Hybrid Features

SCDC-Net: Skin Cancer Detection and Classification using Deep Q Neural Network with Hybrid Features

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