Corn Leaf Disease Classification Using Local Binary Patterns (LBP) Feature Extraction

Rachmad, Aeri; Syarief, Mohammad; Rifka, Silfia; Sonata, Fifin; Setiawan, Wahyudi; Rochman, Eka Mala Sari

doi:10.1088/1742-6596/2406/1/012020

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…The calculation of accuracy in machine learning is commonly known as the Confusion Matrix. The Confusion Matrix provides detailed information about the comparison of the classification results performed by the system or model with the actual classification results from the data [24,25]. At this stage, there are several common and frequently used performance matrices, namely accuracy, precision, and recall.…”

Section: Confution Matrixmentioning

confidence: 99%

Comparison of CNN Architectures for Mycobacterium Tuberculosis Classification in Sputum Images

Rachmad,

Syarief,

Hutagalung

et al. 2024

ISI

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Tuberculosis (TB) is a preventable and treatable infectious disease, but remains a serious problem in high-risk countries. Accurate early detection remains a challenge despite prevention efforts. The primary method of detecting tuberculosis is identifying bacteria in sputum samples using a microscope. This research focuses on the use of Convolutional Neural Network (CNN) with the AlexNet, ResNet-18, ResNet-50, and VGG-16 architectures in the early detection and classification of Tuberculosis (TB) through processing images of TB patients' sputum. A dataset of sputum images was collected and processed to ensure quality and adequate representation. Each CNN model was trained using deep learning techniques on the prepared dataset. The aim of this research is to compare the performance of each model in recognizing and classifying sputum images containing Mycobacterium tuberculosis bacteria and those without TB bacteria. The research results show that AlexNet architecture outperforms ResNet-18, ResNet-50 and VGG-16 in classification accuracy of Mycobacterium tuberculosis. The best validation accuracy achieved was 93.42% with the fastest time of 5 minutes and 52 seconds using AlexNet architecture. Identifying the most appropriate AlexNet architectural model could unlock the potential for developing automated systems that efficiently identify TB, thereby enabling faster and more timely medical intervention.

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Section: Confution Matrixmentioning

confidence: 99%

Comparison of CNN Architectures for Mycobacterium Tuberculosis Classification in Sputum Images

Rachmad,

Syarief,

Hutagalung

et al. 2024

ISI

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show abstract

“…Accuracy itself is a value that represents the success rate of the model being built, where the higher the accuracy, the model can provide high accuracy. The calculation to find the accuracy value is done by adding up the true positive (TP) and true negative (TN) classes which are then divided by the total amount of data in each class, here is the formula for calculating the accuracy of the model built [22,23]:…”

Section: Figure 5 Confusion Matrixmentioning

confidence: 99%

Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

Rachmad¹,

Fuad²,

Rochman³

2023

MMEP

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The decline in corn production can affect the continuity of food grown in society, especially in Indonesia, which is a country with a high level of corn consumers. Several factors cause a decrease in the production of corn plants, one of which is unhealthy plants so that their growth slows down and even makes the corn plants not bear fruit or are damaged. Therefore, a system is needed that can identify diseases in corn plants so that appropriate treatment can be carried out as early as possible to prevent severe damage to corn plants. With this research, the system can be built by utilizing machine learning in building a classification system using the Convolutional Neural Network (CNN) algorithm with a dataset of corn leaf images taken from farmers' fields in the Madura Region with four target classes namely healthy, gray leaf spot, blight, and common rust. Testing was carried out using several CNN architectural models such as SqueezeNet, AlexNet, ResNet-101, ResNet-50, and ResNet-18. The parameters used were 5 epochs with 100 iterations, a learning rate of 0.0001, using Adam optimization, and a data distribution of 70% for training data and 30% for testing data. The test results obtained in classifying corn images using the Convolutional Neural Network method with the ResNet-50 architecture provide a very good accuracy value of 95.59%.

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“…A convolution with a size of height x width x channel feature input map will be divided into several groups whose number depends on the number of channels indicating the depth of the network. Meanwhile, pointwise convolution is the opposite of depthwise convolution where the depth of the network depends on the number of input channels [22].…”

Section: Figure 2 Mobilenet Architecturementioning

confidence: 99%

An Automated System for Osteoarthritis Severity Scoring Using Residual Neural Networks

Rachmad,

Sonata,

Hutagalung

et al. 2023

MMEP

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Osteoarthritis (OA) is a chronic disease, characterized by progressive deterioration of cartilage tissue and consequent thinning of the cartilage layer within joints. This degradation leads to an increased likelihood of bone collision during movement, typically manifesting in patients as joint pain, knee swelling, stiffness, and difficulties in executing daily activities. The diagnosis of OA often involves the analysis of physical examination results, patient anamnesis, and additional supportive examinations, which are predominantly conducted manually. Addressing these challenges, this study harnesses Convolutional Neural Network (CNN) algorithms, specifically the Residual Neural Network and Mobile Neural Network architectures, to develop an automated system for classifying OA severity. Utilizing a knee image dataset comprised of 8260 records procured from NDA OAI, the model is trained and tested with a data split of 80% and 20% respectively. The Residual Neural Network (ResNet-101) architecture is employed for model training, utilizing Adam optimization with a learning rate set at 0.0001 over 50 epochs. The resulting model yields a training accuracy of 67.65%, and a validation accuracy of 57.06%. This study demonstrates the potential of CNN methods for automated, accurate classification of OA severity using knee imagery, thus offering a promising avenue for enhancing diagnostic efficiency and precision.

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Corn Leaf Disease Classification Using Local Binary Patterns (LBP) Feature Extraction

Cited by 9 publications

References 6 publications

Comparison of CNN Architectures for Mycobacterium Tuberculosis Classification in Sputum Images

Comparison of CNN Architectures for Mycobacterium Tuberculosis Classification in Sputum Images

Convolutional Neural Network-Based Classification Model of Corn Leaf Disease

An Automated System for Osteoarthritis Severity Scoring Using Residual Neural Networks

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