Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier

Balaji, V.; Suganthi, S.; Rajadevi, R.; Kumar, Vikrant; Balaji, Bharathan; Pandiyan, Sanjeevi

doi:10.1016/j.measurement.2020.107922

Cited by 131 publications

(41 citation statements)

References 13 publications

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“…Finally, a survey of the literature is conducted with an emphasis on machine and transfer learning approaches. Support vector machines [38], random forests [39], and Navie bayes [40] were among the first classification algorithms to be widely used in practice. These tactics were effective for small datasets; however, they were ineffective for the ISIC challenge datasets.…”

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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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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“…Recently, Balaji et al (2020) presented a dynamic graph cut algorithm and Naive Bayes classifier for the segmentation and classification of skin disease using ISIC 2017 data set. Three types of cancers keratosis, melanoma, benign examined and 92.9, 91.2, 94.3% classification accuracy reported.…”

Section: Handcrafted Features For Traditional Machine Learning‐based Classificationmentioning

confidence: 99%

Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features

Saba

2021

Microscopy Res & Technique

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Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the riskiest is melanoma, although it starts in a few different ways.The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non-handcrafted features. Additionally, clinical features such as Menzies method, seven-point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted.

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“…Yang mana : Hasil penelitian oleh [7], [11], [12], [14] menunjukkan bagaimana pada Naïve Bayes sukses mengklasifikasikan data dengan akurasi klasifikasi yang tinggi. Namun penelitianpenelitian tersebut biasanya menggunakan data-data dengan fitur yang sedikit atau jumlah data yang kecil.…”

Section: Iunclassified

Studi Literatur Tentang Performa Naïve Bayes Dalam Klasifikasi Data

Sihananto

Maulana²

2021

santika

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Klasifikasi data merupakan salah satu proses penting dalam pengolahan data. Salah satu metode klasifikasi data yang populer adalah Naïve Bayes yang dikenal karena mampu mengklasifikasi data secara cepat. Meski begitu ketika dihadapkan pada data-data tertentu akurasi klasifikasi dari Naïve Bayes sangat rendah. Dari hasil studi literatur yang telah dilakukan ditemukan fakta bahwa untuk data-data dengan atribut yang tidak terlalu banyak dan setiap atributnya memiliki bobot yang sama Naïve Bayes masih dapat melakukan klasifikasi dengan akurasi tinggi. Untuk data-data dengan bobot atribut yang berbeda-beda, dilakukan modifikasi atau hibridisasi terhadap Naïve Bayes. Hasil dari modifikasi ini berhasil meningkatkan akurasi klasifikasi Naïve Bayes.

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Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier

Cited by 131 publications

References 13 publications

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

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

Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features

Studi Literatur Tentang Performa Naïve Bayes Dalam Klasifikasi Data

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