The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Coffee Variety

Mengistu, Abrham Debasu

doi:10.12928/telkomnika.v16i2.8419

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…The work was that the effective useful features of images were extracted to guide the statistical calculation to monitor further. Yet another work using belief network was for breast cancer prediction from image and it was discussed (Mengistu, 2018) as follows. The unsupervised and supervised algorithm has been utilized for pre‐training and fine‐tuning processes respectively for automation feature extraction using this belief network model.…”

Section: Methodsmentioning

confidence: 99%

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A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

et al. 2021

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Background The COVID‐19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare infrastructure and accessibility the world over. Consequently, the importance of timely prediction and treatment of the disease to reduce transmission and mortality rates cannot be emphasized enough. Various symptoms of the disease have been identified as it progresses from the time it is contracted. COVID‐19 has been found to internally affect the lungs, and the four progressive stages of the infection can be categorized as mild, moderate, severe, and critical. Therefore, an accurate analysis of the current stage of the disease that can help predict its progression has become critical. X‐ray imaging has been found to be an effective screening procedure for predicting the various stages of this epidemic. Although many different approaches using machine learning, as well as deep learning were utilized to predict and classify diseases in general, till date, such an approach has not been used to predict the various stages of COVID‐19 by using X‐ray imaging to identify and classify those stages. Materials and method The proposed hybrid method used three public datasets for its implementation. In this work, extensive images were used for the purposes of testing and training. The dataset‐1 consists of 1200 COVID‐19 as well as 1200 Non‐COVID‐19 images, while dataset‐2 used 700 COVID‐19 as well as 700 Non‐COVID‐19 images, and finally, dataset‐III utilized 1900 COVID‐19 as well as 1900 Non‐COVID‐19 images for purposes of testing and training. The proposed work undertook the task of pre‐processing using textual and morphological features, while the segmentation and prediction of COVID‐19 as well as Non‐COVID‐19 images were undertaken using VGG‐16 with light GBM for better prediction and handing of huge datasets, and finally, the classification of the various stages of COVID‐19 images was performed using Deep Belief Network. Results The outcomes of the proposed work were subjected to several iterations which were then compared using different parameters such as accuracy, specificity, and sensitivity. In general, the prediction and grouping of the various stages of COVID‐19 by using affected images were found to be 99.2%, 99.4% and 99.5%, respectively. The bacterial pneumonia prediction rates were observed to be 98.5%, 99.4% and 98.3%, respectively. The average classification of the stages were found to be 98.1%, 98.6% and 98.3%, while the combined multi‐classification prediction rates were observed to be 98.6%, 99.1% and 98.7%, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Stages Of Covid-19 X-ray Imagesmentioning

confidence: 99%

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

et al. 2021

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“…The most optimal result is segmentation using Fuzzy C Mean and Backpropagation Neural Network recognition algorithm. The level of accuracy is 94.54% [10]. Scanning of coffee fakes using digital images and SPA-LDA (linear discriminant analysis).…”

Section: Related Workmentioning

confidence: 99%

Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image

Waliyansyah

Hasbullah

2021

emitter

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Coffee is one of the many favorite drinks of Indonesians. In Indonesia there are 2 types of coffee, namely Arabica & Robusta. The classification of coffee beans is usually done in a traditional way & depends on the human senses. However, the human senses are often inconsistent, because it depends on the mental or physical condition in question at that time, and only qualitative measures can be determined. In this study, to classify coffee beans is done by digital image processing. The parameters used are texture analysis using the Gray Level Coocurrence Matrix (GLCM) method with 4 features, namely Energy, Correlation, Homogeneity & Contrast. For feature extraction using a classification algorithm, namely Naïve Bayes, Tree, Support Vector Machine (SVM) and Logistic Regression. The evaluation of the coffee bean classification model uses the following parameters: AUC, F1, CA, precision & recall. The dataset used is 29 images of Arabica coffee beans and 29 images of Robusta beans. To test the accuracy of the model using Cross Validation. The results obtained will be evaluated using the confusion Matrix. Based on the results of testing and evaluation of the model, it is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1 = 0.983, Precision = 0.983 and Recall = 0.983.

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“…Each kind of voice has its exceptional qualities called highlight and the way toward removing these highlights from the individual voice is called highlight extraction. The voice highlights which are removed are contrasted and right now spared voices in the database for coordinating [13,14].…”

Section: Voice Acknowledgment Modulementioning

confidence: 99%

Design and implementation a security system for bank using voice recognition

Ali¹,

Azze²

2019

IJPEDS

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In this work, a new security system design is demonstrated based on the sound signal for bank protection. Voice Acknowledgment Module is placed on the gate of the safe. Voice Acknowledgment Module is set to perceive the capable client as indicated by his sound recurrence reaction. The plan could program to permit tow clients for greater adaptability the card location strategy is executed utilizing individual data information to permit other client in the framework. A delicate camera is accustomed to observing the developments in the structure and for the reasons for wellbeing and security. All these sensors are controlled by arduino.<strong> </strong>

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The Effects of Segmentation Techniques in Digital Image Based Identification of Ethiopian Coffee Variety

Cited by 6 publications

References 10 publications

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image

Design and implementation a security system for bank using voice recognition

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