Peningkatan Kualitas Citra Foto Rontgen Sebagai Media Deteksi Kanker Paru

Listyalina, Latifah

doi:10.35842/jtir.v12i34.1

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…The gray scaling process is converting a full-color (RGB) image to a grayscale form. The equation used in the grayscale process is as follows [13]: Adaptive Histogram Equalization is one of the image processing techniques to produce uniform images [14]. Figure 2.…”

Section: B Image Processingmentioning

confidence: 99%

Identification of Animal Meat as Halal Product Authentication Using the Box Counting Method

Ratri

Febrita²,

Ratri³

et al. 2023

Jartel

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Awareness and the need for authentication or assurance of the authenticity of halal foods is a challenge in many countries, especially in Muslim countries. So, many countries where the majority of the population is Muslim are quite strict in ensuring the sale and purchase of meat, especially in traditional markets. Therefore, this study aim is to find a system to identify the types of meat from the image, such as the initiation of the manufacture of practical technology for the authentication of halal products. In this study, the box-counting method was used to determine the fractal dimension value of the identified animal flesh image. The container counting method uses the texture of the animal’s meat that has unique characteristics of each type. The results of the identification of the animal meat obtained were 24 tests which were correctly identified from a total of 36 test data. This translates into a 66,67% success rate for the system.

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Section: B Image Processingmentioning

confidence: 99%

Identification of Animal Meat as Halal Product Authentication Using the Box Counting Method

Ratri

Febrita²,

Ratri³

et al. 2023

Jartel

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show abstract

“…Medical images have an important role in classifying or identifying a disease. One of the most frequently used techniques is X-ray Photo [1] [2] [3]. This technique is used by radiologists to be able to see and find out information about the patient's body condition.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Multilayer Perceptron Hyperparameter in Classifying Pneumonia Disease Through X-Ray Images with Speeded-Up Robust Features Extraction Method

Ula¹,

Muhathir²,

Sahputra³

2022

IJACSA

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Pneumonia is an illness that affects practically everyone, from children to the elderly. Pneumonia is an infectious disease caused by viruses, bacteria, or fungi that affect the lungs. It is quite difficult to recognize someone who has pneumonia. This is because pneumonia has multiple levels of classification, and so the symptoms experienced may vary. The multilayer perceptron approach will be used in this study to categorize Pneumonia and determine the level of accuracy, which will contribute to scientific development. The Multilayer Perceptron is employed as the classification method with hyperparameter learning rate and momentum, while SURF is used to extract the features in this classification. Based on the experiments that have been carried out, in general, the learning rate value is not very influential in the learning process, both at the momentum values of 0.1, 0.3, 0.5, 0.7, and 0.9. The best desirable accuracy value for momentum 0.1 is at a learning rate of 0.05. The best desirable accuracy value for momentum 0.3 is at a learning rate of 0.09. The most desirable accuracy value for momentum 0.3 is at a learning rate of 0.05 and 0.07. At a learning rate of 0.03 the highest ideal accuracy value is obtained. The best desirable accuracy value for momentum 0.9 is at a learning rate of 0.09. this research should be redone using the number of hidden layers and nodes in each hidden layer. The addition of a hidden layer, as well as variations in the number of nodes in the hidden layer, will affect computation time and yield more optimal accuracy results.

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Determining Image Opacity in Broiler Respiratory Radiographic Using ImageJ and Ansel Adam’s Zone System

Wirahadikesuma¹,

Santoso²,

Maheshwari³

et al. 2020

J. Riset Vet. Ind.

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The diagnosis of diseases of the respiratory, lung, and air sacs generally uses radiographic images by radiologists. Therefore, the results are very subjective, causing differences in the interpretation and diagnosis among different radiologists. A radiographic image reading needs to be made in the form of a simple, fast, and accurate algorithm. The study aimed to reduce subjectivity and be easily carried out by radiology medical personnel, especially veterinarians. This study carried out density measurements by image processing using ImageJ software on 14 radiographic images of broiler chickens. Furthermore, the density value is associated with the Ansel Adam's - grayscale system to determine the opacity of respiratory tract tissues/organs, which were previously inhaled by one of them with chitosan-iopamidol nanoparticles using a nebulizer. The results of density measurements for the category of opacity in radiographic images are that seven spot areas lead brighter (radiopaque) only in chickens that are inhaled mist maker of chitosan-iopamidol nanoparticles. Then the determination of the value range is obtained average value on two ventrodorsal radiographic images, which are inhaled by mist maker of chitosan-iopamidol nanoparticles and chitosan nanoparticle compressor. The conclusion of this study was only in chickens that were infested with mist maker chitosan-iopamidol, whose radiographic image had a radiopaque spot and middle-value area.

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Peningkatan Kualitas Citra Foto Rontgen Sebagai Media Deteksi Kanker Paru

Cited by 3 publications

References 1 publication

Identification of Animal Meat as Halal Product Authentication Using the Box Counting Method

Identification of Animal Meat as Halal Product Authentication Using the Box Counting Method

Optimization of Multilayer Perceptron Hyperparameter in Classifying Pneumonia Disease Through X-Ray Images with Speeded-Up Robust Features Extraction Method

Determining Image Opacity in Broiler Respiratory Radiographic Using ImageJ and Ansel Adam’s Zone System

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