Analisis Performa Algoritma Naive Bayes Pada Deteksi Otomatis Citra Mri

Abstract

The brain in humans becomes part of the central nervous system of the human body. The use of imaging with MRI is one that can be used as a first step to detect parts of the human brain. The imaging step is the first step in diagnosing brain tumor. By performing feature extraction, which aims to process the classification of brain tumors, between normal and abnormal brain images using the naive Bayes method. Obtained 41 images which then became 39 datasets. Feature extraction results with 2 classes, normal as many as 20 data and abnormal data 19. The calculation results obtained the value of the normal class of 0.513 and the abnormal class of 0.487 the value of the calculation accuracy of 84.17%.

Identifikasi Tumor Otak Citra MRI dengan Convolutional Neural Network

“…Ada banyak metode yang digunakan dalam beberapa penelitian terkait ekstraksi fitur dan klasifikasi tumor otak. Metode ekstrasi fitur pada penelitian sebelumnya diantaranya geometri (luas tumor, luas tempurung), dan statistik nilai grayscale citra [2]; LDA (Linear Discriminant Analysis) [3]; eigenbrain otak [4]; Wavelet Transform [5], [6], [7]; Histogram of Oriented Gradient (HOG) [8]; Grey-level co-occurrence matrix (GLCM) [9].…”

“…Berbagai metodologi telah dikembangkan untuk identifikasi tumor otak *) penulis korespondensi: Nur Nafi'iyah Email: mynaff@unisla.ac.id Beberapa penelitian identifikasi tumor otak: Mengklasifikasi tumor otak (Glioma, Meningioma, and Pituitary) dengan CNN [1]. Mengklasifikasi jenis tumor (normal dan abnormal) berdasarkan fitur geometri dengan metode Naive Bayes [2]. Mengklasifikasi tumor dengan metode SVM dari ekstraksi ciri LDA citra [3].…”

Modification CNN Transfer Learning for Classification MRI Brain Tumor

“…The human brain is important because it is the body's nerve center. By utilizing MRI (Magnetic Resonance Imaging), imaging technology or magnetic resonance can be used to detect tumor disease in humans (Akbar et al, 2019). Can use MRI images of the brain for the initial stages in analyzing diseases of the brain as well as considerations in operating on the brain.…”

“…The stages in diagnosing or identifying diseases based on computer vision science include feature extraction or characteristics of image objects so that they can recognize patterns from an object and can be used for diagnosis. The feature extraction methods used in brain images in previous studies were tumor area, brain area, and presentation of tumor area to brain area, mean value, standard deviation, entropy, and variance of brain images (Akbar et al, 2019); Another feature used is LDA (Linear Discriminant Analysis) (Adinegoro et al, 2015); image mean value feature, image eigenbrain http://dx.doi.org/10.35671/telematika.v16i2.2272 (Soesanti et al, 2011); PCA (Principle Component Analysis) feature (Susmikanti, 2010); the feature used is the value GLCM (Gray Level Co-occurrence Matrix) (Widhiarso et al, 2018); another feature is the DWT (Discrete Wavelet Transformation) (Astuti, 2019), (Varuna Shree & Kumar, 2018), (Kumar et al, 2017; Histogram of Oriented Gradient (HOG) (M & Azizah, 2022); texture features with values of Contrast, Correlation, Energy, Dissimilarity, ASM (Angular Second Moment), Homogeneity, and Entropy (Febrianti et al, 2020).…”

“…Finding malignancies on brain MRI pictures or extracting features from the images has been the subject of numerous research. Several methods are used to identify or diagnose brain tumors Naive Bayes (Akbar et al, 2019); SVM (Adinegoro et al, 2015), (Kumar et al, 2017), (M & Azizah, 2022), (Febrianti et al, 2020); Multi-Layer Neural Network (Susmikanti, 2010), (Astuti, 2019), (Varuna Shree & Kumar, 2018), (Gu & Li, 2021); CNN (Convolution Neural Network) or Deep Learning (Widhiarso et al, 2018), (Tjahyaningtijas et al, 2021), (Irmak, 2021), (Aamir et al, 2022), (Harish & Baskar, 2020), (Abiwinanda et al, 2020), (Daniel & Ruxandra, 2021), (Deepak & Ameer, 2019), (Yuliawan & 'Uyun, 2022).…”