Selection of optimal SINR threshold in fractional frequency reuse by comparing Otsu's and entropy method

Onim, Antony; Musyoki, Stephen; Kihato, P.

doi:10.1016/j.heliyon.2022.e11736

Cited by 1 publication

(1 citation statement)

References 15 publications

(23 reference statements)

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“…Hasil data spike ini kemudian digunakan sebagai input ke dalam algoritma SNN untuk training data sehingga nantinya digunakan sebagai sistem klasifikasi. Pada penelitian ini tahap segmentasi citra dilakukan dengan menggunakan metode Otsu, yaitu membagi histogram citra grey scale kedalam dua daerah yang berbeda secara otomatis untuk melakukan perhitungan rerata intensitas citra pada kedua daerah tersebut (Onim et al, 2022).…”

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Utilization of Spiking Neural Network (SNN) in X-Ray Image for Lung Disease Detection

Ningtias,

Rofi’i,

Pramudita

2024

JITE

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The large number of cases of lung disease means that doctors have difficulty in making initial diagnoses, making them prone to misdiagnoses. One type of lung disease that is included in the vulnerable category is pneumonia. Early detection of the condition of the lungs affected by bacterial pneumonia can be carried out by screening using the X-Ray examination modality, namely Digital Radiography (DR). However, in practice, the diagnosis process on Citra DR takes a long time because it requires competent medical personnel (specialists). A system is needed that can help medical personnel to speed up the process of diagnosing lung disease and get accurate results so that misdiagnosis does not occur. The aim of this research is to utilize the Spiking Neural Network (SNN) method for classifying lung disease from DR images. The system was created using MATLAB with the initial step of creating a read data program, namely reading DR image secondary data in .jpg format taken from Kaggle.com. This research uses DR image data totaling 200 images. Next, standardize the size to 50 x 50 pixels. Then segmenting the image divides the gray level histogram into two different parts of the image automatically without requiring user assistance to enter threshold values for normal and pneumonia images. Then convert the image to 1 dimension and create a manual program for the training data using 50 normal images and 50 pneumonia images. Lastly, create a program to test the data using 100 normal images and 100 pneumonia images. Based on the results of data testing, a confusion matrix was obtained from 200 images with sensitivity of 87%, specificity of 69%, precision of 73.7288%, recall of 69%, and accuracy of 78%

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Section: Metode Penelitian Gambar 3 Flowchart Penelitianunclassified