Detection of Cancerous and Non-cancerous Skin by using GLCM Matrix and Neural Network Classifier

Al-Amin, Md.; Miah, Mohammad Badrul Alam; Mia, Md. Ronju

doi:10.5120/ijca2015907513

Cited by 11 publications

(7 citation statements)

References 4 publications

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“…The keyphrase extraction technique is counted as a binary classification problem [1] using this method from articles, with a proportion of candidate keyphrases categorised as keyphrases and non-keyphrase. Methods for solving the classification problem include support vector machines, Decision trees, Naive Bayes [3], Neural networks [17], [18], and C4.5 [19]. The prominent techniques are examined in detail in the subsequence that adopts this method.…”

Section: A Supervised Methodsmentioning

confidence: 99%

Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

Miah¹,

Awang²,

Azad³

et al. 2022

IJACSA

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The extraction of high-quality keywords and summarising documents at a high level has become more difficult in current research due to technological advancements and the exponential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a highlevel need to use features for the keyphrase extraction, becoming more popular. A new unsupervised keyphrase concentrated area (KCA) identification approach is proposed in this study as a feature of keyphrase extraction: corpus, domain and language independent; document length-free; utilized by both supervised and unsupervised techniques. In the proposed system, there are three phases: data pre-processing, data processing, and KCA identification. The system employs various text pre-processing methods before transferring the acquired datasets to the data processing step. The pre-processed data is subsequently used during the data processing step. The statistical approaches, curve plotting, and curve fitting technique are applied in the KCA identification step. The proposed system is then tested and evaluated using benchmark datasets collected from various sources. To demonstrate our proposed approach's effectiveness, merits, and significance, we compared it with other proposed techniques. The experimental results on eleven (11) datasets show that the proposed approach effectively recognizes the KCA from articles as well as significantly enhances the current keyphrase extraction methods based on various text sizes, languages, and domains.

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Section: A Supervised Methodsmentioning

confidence: 99%

Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

Miah¹,

Awang²,

Azad³

et al. 2022

IJACSA

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“…In addition, the gray level co-occurrence matrix is used to extract global texture features. 30 In contrast, color feature extraction is performed employing histogram. 31…”

Section: Feature Extractionmentioning

confidence: 99%

A hybrid feature fusion strategy for early fusion and majority voting for late fusion towards melanocytic skin lesion detection

Singh

Janghel

Sahu

2021

Int J Imaging Syst Tech

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A computer-aided-diagnostic system for diagnosing melanoma often uses distinct kinds of features for characterizing the lesions. Extracting distinct features from melanocytic images represents the various characteristics of pigmented lesions. Concatenating such features distinguishes extracted feature's information effectively while eliminating the redundant information amid them, which aids in discriminating the cancerous and noncancerous lesions. This article proposes a framework comprising segmentation, feature extraction, feature fusion, and classification to differentiate benign lesions from melanoma. The proposed framework is four-fold: beginning with the extraction of ROI from an image, the SLICACO method is used for segmentation. Thereafter, ABCD rule-based global and local features are extracted for effective melanoma detection. Further, we develop a new hybrid feature fusion strategy, PCAFA, leveraging the benefits of principal component analysis and factor analysis. The method performs early fusion by combining all extracted features within an individual feature vector and fed to a learning model for the prediction. While late fusion is performed using majority voting by combining the outputs of machine learning models. After that, gradient tree boosting, support vector machine, and decision tree models are trained to utilize distinct features of skin lesions for their classification as benign or malignant. The effectiveness of the designed framework is validated on the ISIC2017 benchmark skin lesion dataset based on specificity, sensitivity, and accuracy. The generalizability of the designed framework is gauged by conducting a fair comparison with conventional methods. Evaluated results reveal the potential of the proposed fused feature-set in discriminating malignant and nonmalignant lesions with an accuracy of 96.8%.

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“…Various segmentation approaches were used to the dermoscopic images to segment the skin lesions and calculated with 3 different metrics, such as sensitivity, accuracy and border error. Segmentation performance shows that Neural Network based lesion segmentation has high sensitivity, accuracy and less border error [12] [13].…”

Section: Related Workmentioning

confidence: 99%

Classification of Cancerous Skin using Artificial Neural Network Classifier

Zakareya¹,

Badrul²,

Arafat³

2018

IJCA

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Cancer is one of the most hazardous diseases that cause death. However, if detected early this medical condition is not very prohibitive to defeat. The skin cancer is the anomalous growth of skin cells most often promotes on body apparent to the sunlight but can occur anywhere on the body. Skin cancer is the most common type of malignant tumor in both men and women. So, for the detection of cancer, image processing approaches play a paramount role. There are mainly four steps involved in the detection of skin cancer that are: Preprocessing, segmentation, feature extraction, and classification. The Neural network is used to classify images. It is an easy system rather than taking a biopsy from a doctor. The system consumes less time and gets the better result than the ordinary system.

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Detection of Cancerous and Non-cancerous Skin by using GLCM Matrix and Neural Network Classifier

Cited by 11 publications

References 4 publications

Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

A hybrid feature fusion strategy for early fusion and majority voting for late fusion towards melanocytic skin lesion detection

Classification of Cancerous Skin using Artificial Neural Network Classifier

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