An Efficient Gray-Level Co-Occurrence Matrix (GLCM) based Approach Towards Classification of Skin Lesion

Abbas, Zeeshan; Rehman, M Atique ur; Najam, Shahzaib; Rizvi, Saad

doi:10.1109/aicai.2019.8701374

Cited by 36 publications

(24 citation statements)

References 13 publications

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“…Galloway [27,28] introduced the Gray Level Run Length Matrix (GLRM), a section of gray also known as run length. It can be described as a linear multitude of continuous pixels with the same gray level in a particular direction.…”

Section: Gray Level Run-length Matrix (Glrlm)mentioning

confidence: 99%

The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

Naeem¹,

Ali

Chesneau

et al. 2021

Agronomy

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This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.

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Section: Gray Level Run-length Matrix (Glrlm)mentioning

confidence: 99%

The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

Naeem¹,

Ali

Chesneau

et al. 2021

Agronomy

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“…Abbas et al [12] used the traditional machine learning approach for classification of benign and malignant skin lesions. In their proposed methodology, 900 images were used.…”

Section: Literature Reviewmentioning

confidence: 99%

Mobile-based Skin Lesions Classification Using Convolution Neural Network

Hameed¹,

Shabut²,

Hameed³

et al. 2020

AETiC

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This research work is aimed at investing skin lesions classification problem using Convolution Neural Network (CNN) using cloud-server architecture. Using the cloud services and CNN, a real-time mobile-enabled skin lesions classification expert system “i-Rash” is proposed and developed. i-Rash aimed at early diagnosis of acne, eczema and psoriasis at remote locations. The classification model used in the “i-Rash” is developed using the CNN model “SqueezeNet”. The transfer learning approach is used for training the classification model and model is trained and tested on 1856 images. The benefit of using SqueezeNet results in a limited size of the trained model i.e. only 3 MB. For classifying new image, cloud-based architecture is used, and the trained model is deployed on a server. A new image is classified in fractions of seconds with overall accuracy, sensitivity and specificity of 97.21%, 94.42% and 98.14% respectively. i-Rash can serve in initial classification of skin lesions, hence, can play a very important role early classification of skin lesions for people living in remote areas.

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“…Machine learning and handcrafted features have contributed greatly in different complicated research problems like image quality assessment [11], [12], human activity classification [13] and medical diagnostic systems [14], [15]. Similarly, in literature research can be found where handcrafted features and machine learning algorithms are adopted for m6A identification task.…”

Section: Introductionmentioning

confidence: 99%

m6A-NeuralTool: Convolution Neural Tool for RNA N6-Methyladenosine Site Identification in Different Species

Rehman

Kim²,

Tayara

et al. 2021

IEEE Access

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An important role is played by N6-methyladenosine (m6A) in RNA methylation modification. The modification information is crucially required for development in the field of medicine. Biochemical experiments for m6A identification have demonstrated high-quality results. However, this process is not a feasible solution due to its cost and time constraints. Recently, artificial intelligence has played a role to produce effective models which can be utilized for speedy and efficient identification of N6-methyladenosine sites. In consensus, a computational model named as m6A-NeuralTool is proposed in this study. The m6A-NeuralTool makes the final prediction for the identification of m6A sites by applying majority voting on three different sub-architectures. These sub-architectures uses a set of Convolution layers to extract the important features from the one-hot encoded input sequence. Further, one of the subarchitectures uses fully connected layers for the classification while the other two uses Support Vector Machine and Naive Bayes. The proposed m6A-NeuralTool is evaluated on four different species datasets using 10-fold cross-validation, where the proposed tool outperformed the existing state-of-art models for modification identification of m6A sites. When compared with existing models the proposed model increased the accuracy by 14.8%, 8.8%, 6.3%, and 4.9% for the datasets of Saccharomyces cerevisiae (S. cerevisiae), Arabidopsis thaliana (A. thaliana), Mus musculus (M. musculus) and Homo sapiens (H. sapiens) species respectively. The proposed model will allow the researchers from the field of bioinformatics and medicine to accurately identify the modification in m6A sites and use the information in the development of different products which would be beneficial for the community. The m6A-NeuralTool can be accessed freely at: http://nsclbio.jbnu.ac.kr/tools/m6A-NeuralTool/ INDEX TERMS RNA N6-Methyladenosine, Convolution Neural Network (CNN), Support Vector Machine (SVM), Naive Bayes (NB), Computational Biology

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An Efficient Gray-Level Co-Occurrence Matrix (GLCM) based Approach Towards Classification of Skin Lesion

Cited by 36 publications

References 13 publications

The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

Mobile-based Skin Lesions Classification Using Convolution Neural Network

m6A-NeuralTool: Convolution Neural Tool for RNA N6-Methyladenosine Site Identification in Different Species

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