Identification of Madura Tobacco Leaf Disease Using Gray- Level Co-Occurrence Matrix, Color Moments and Naïve Bayes

Damayanti, Fitri; Muntasa, Arif; Herawati, Sri; Yusuf, Muhammad; Rachmad, Aeri

doi:10.1088/1742-6596/1477/5/052054

Cited by 13 publications

(9 citation statements)

References 2 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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“…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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“…Color moment (CM) is a computation technique utilized to discriminate images based on their color distribution in the image similar to the central tendency of the probability distribution. It is a potential technique for the description of color features [26,27]. Once determined, these moments contribute a quantity for color resemblance among images.…”

Section: Extraction Of Color Texture Featuresmentioning

confidence: 99%

Detection of Diabetic Foot Using Statistical Features

Jayapal¹,

Murugesan²,

Mohan³

2023

Diabetic Foot - Recent Advances

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Diabetes is a serious threat to universal health that respects neither socioeconomic rank nor nationwide boundaries. Diabetic foot and lower extremities problems, which affect 40 to 60 million people with diabetes universally, are a significant source of morbidity in people with diabetes. Conducting regular screening and risk stratification for at-risk feet can be greatly used for the management of blood glucose levels. Recent studies revealed that qualitative evidence can be attained using temperature variations from the thermogram of the plantar foot. The changes in temperature distribution are vital in the investigation of diabetic foot, which assist in the early detection of foot ulceration. The main objective of this work is to perform statistical analysis of diabetic foot to draw reasonable and accurate inferences. Besides, there is no gold standard method in classifying the plantar thermal images into any particular group. This may be conquered by quantitatively analyzing the temperature distributions in each foot separately. Since, plantar thermal images are colored in nature, certain color statistical features which are statistically more significant are added with the quantitative temperature distribution to develop an efficient machine learning method to prognosticate the likelihood of diabetes in patients with maximum accuracy is explored.

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Identification of Madura Tobacco Leaf Disease Using Gray- Level Co-Occurrence Matrix, Color Moments and Naïve Bayes

Cited by 13 publications

References 2 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

Detection of Diabetic Foot Using Statistical Features

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