Method to Enhance Classification of Skin Cancer Using Back Propagated Artificial Neural Network

Nyemeesha, V.; Ismail, B. Mohammed

doi:10.1007/978-3-030-82269-9_9

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…The authors of the study [46] found that using Ensemble hybrid CNN (HECNN) was effective in categorizing skin lesions into a wide variety of categories. In addition, the creation of DenseNet, SENet, and ResNeXt (DSR-Net) [47] has resulted in an improvement in the overall performance of machine learning models in comparison to prior models. In both the ISIC-2018 and ISIC-2019 competitions, this work took first and second prize, respectively.…”

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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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 diagnostic accuracy results demonstrated a wide range, with fair sensitivity for melanoma but significantly less for keratinocyte carcinomas. In [33], the authors focus mostly on leveraging mobile health to identify cancer. There are several worries about privacy and accuracy in the field of mobile health.…”

Section: Dataset Attributesmentioning

confidence: 99%

Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning

et al. 2023

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Skin cancer represents one of the most lethal and prevalent types of cancer observed in the human population. When diagnosed in its early stages, melanoma, a form of skin cancer, can be effectively treated and cured. Machine learning algorithms play a crucial role in facilitating the timely detection of skin cancer and aiding in the accurate diagnosis and appropriate treatment of patients. However, the implementation of traditional machine learning approaches for skin disease diagnosis is impeded by privacy regulations, which necessitate centralized processing of patient data in cloud environments. To overcome the challenges associated with data privacy, federated learning emerges as a promising solution, enabling the development of privacy-aware healthcare systems for skin cancer diagnosis. This paper presents a comprehensive review that examines the obstacles faced by conventional machine learning algorithms and explores the integration of federated learning in the context of privacy-conscious skin cancer prediction healthcare systems. It provides discussion on the various datasets available for skin cancer prediction and provides a performance comparison of various machine learning and federated learning techniques for skin lesion prediction. The objective is to highlight the advantages offered by federated learning and its potential for addressing privacy concerns in the realm of skin cancer diagnosis.

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“…In [46], the authors used Ensemble hybrid CNN(HECNN) for multi-class skin lesion classification, and they found it to be effective. Furthermore, DenseNet, SENet, and ResNeXt (DSR-Net) [47] have been developed, which improve the performance of machine learning models compared to previous models. This work came in second place in the ISIC-2018 competition and first place in the ISIC-2019 competition.…”

Section: Literature Surveymentioning

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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Method to Enhance Classification of Skin Cancer Using Back Propagated Artificial Neural Network

Cited by 4 publications

References 45 publications

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

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

Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning

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

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