High-performance visual geometric group deep learning architectures for MRI brain tumor classification

Veni, N.; Manjula, J.

doi:10.1007/s11227-022-04384-9

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Penelitian ini akan melakukan komparasi algoritma Support Vector Machine (SVM), K-Nearest Neighbors (KNN) dan Multilayer Perceptron (MLP) dalam klasifikasi penyakit daun pada tanaman jagung menggunakan model deep learning VGG-16 sebagai ekstraksi fitur citra berdasarkan penelitian Veni dan Manjula [9] model deep learning VGG-16 membuktikan dapat meningkatkan performa akurasi. Dari beberapa arsitektur deep learning seperti Xception, MobileNet V1, Resnet50, dan VGG-16 berdasarkan pada penelitian Prasetyo, dkk [10] membuktikan bahwa model VGG-16 memiliki akurasi terbaik.…”

Section: Pendahuluanunclassified

Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron

Kusuma¹,

Rubianto²,

Rosnelly³

et al. 2023

J. Appl. Comput. Sci. Technol.

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Corn is one of the substitute staple foods in Indonesia after rice. Maize crops grown in Indonesia often experience considerable losses due to maize plant diseases. Generally, plant diseases are initially caused by morphological changes in the leaves. Accurate detection and classification of diseases that appear on the leaves will prevent the widespread spread of the disease. This study will compare classification algorithms, namely Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron to find the best algorithm in the classification of leaf disease in corn plants, namely, cercospora leaf spot gray, common rust, and northern leaf blight using the VGG-16 deep learning model used as image feature extraction. The results showed that the Multilayer Perceptron algorithm produced the best values with accuracy, precision, and recall of 97.4% each.

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Section: Pendahuluanunclassified

Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron

Kusuma¹,

Rubianto²,

Rosnelly³

et al. 2023

J. Appl. Comput. Sci. Technol.

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“…In this study, brain tumors that will be classified based on grade are glioma brain tumors measuring 224 x 224. Glioma MRI is classified into 3 classes, namely Grade II, Grade III, and Grade IV and this study added Histogram Equalization (HE) to the preprocessing because of the research conducted by [27], using a preprocessing method for images such as Histogram Equalization (HE) can improve the accuracy of the CNN model because the model can recognize images better and reduce image recognition errors caused by changes in lighting conditions. The first step is preprocessing using Histogram Equalization (HE), then the results of the preprocessing are entered into the VGG19 architecture with the first step the feature space is formed using several 3×3 convolution filters and then reduced by pooling layers in one block.…”

Section: International Journal On Recent and Innovationmentioning

confidence: 99%

An Automatic Detection of Brain Tumor using CNN & VGG19

Mallick,

Mishra

2023

IJRITCC

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According to the 2019 cancer statistics by WHO, brain tumors are considered the main cause of mortality related to cancer throughout the world and are known as one of the most common forms of cancer both in children as well as adults. Among the most common brain tumors, we have those that begin and tend to remain in the brain, which as meningioma with 34% of presence, another type of tumor is called glioma, arising from the surrounding tissue in the brain, it is part of 30% of all tumors in the brain, however, this glioma represents 80% of malignant tumors, making it the most common tumor common that causes death. However, this scheme depicts how convolutional neural networks using VGG19 model can provide an effective mechanism to detect brain tumors at an early stage using MRI images and can save the lives of mankind. Consequently, this research classified glioma brain tumor images using VGG-19 with HE preprocessing data. The model was tested to get a comparison of accuracy, precision, recall, and f1-score of the two test data, namely the original data and HE data. Based on the results of model testing, we can see in table2 that the original data produced the highest values of accuracy, precision, recall, and F1-score, with values of 97% accuracy, 100% precision, 97% recall, and 98% f1 score. While data using HE preprocessing has an accuracy value of 92%, precision of 100%, recall of 92%, and f1 score of 96%t..

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“…Penelitian terkait yang menjadi landasan penerapan deep learning dengan metode Convolutional Neural Netrwork (CNN) modified untuk klasifikasi tumor otak yaitu seperti penelitian yang dilakukan oleh [4] mengklasifikasikan jenis tumor otak menjadi tiga yaitu meningioma, glioma, dan pituitary dan juga mengklasifikasi grade dari tumor glioma menjadi tiga kelas yaitu grade II, grade III, dan grade IV dengan akurasi masing-masing 96,13% dan 98,7%. Penelitian selanjutnya dilakukan oleh [13], dengan mengklasifikasi MRI tumor otak dengan menggunakan 4 arsitektur VGG, yaitu VGG-11,VGG-13,VGG-16 dan VGG-19. Pada penelitian ini membuktikan bahwa transfer learning pada arsitektur VGG memiliki potensi yang baik untuk klasifikasi MRI kanker otak.…”

Section: Pendahuluanunclassified

“…Tahapan pengumpulan data pada penelitian ini yaitu mencari data MRI tumor glioma yang didapatkan dari TCIA (The Cancer Image Archive) public access repository, Repository of Molecular Brain Neoplasia Data (REMBRANDT) [13]. Data yang didapat berupa data dalam format dcim kemudian dilakukan convert data sehingga format menjadi jpg.…”

Section: Pengumpulan Dataunclassified

Penerapan Deep Learning Menggunakan VGG-16 untuk Klasifikasi Citra Glioma

Putri

Negara

Sanjaya

2022

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One of the types of brain tumors in humans is glioma. Glioma is considered to be the most common type of primary brain tumor in adults. To determine the follow-up action that will be carried out by the doctor, the level of glioma needs to be known first. Glioma is divided into 3 grades. To be able to distinguish grades from gliomas, a classification process can be carried out using deep learning with CNN architecture. Glioma grade classification applies Histogram Equalization (HE) preprocessing. The training model uses CNN with the VGG-16 architecture. data using split data with a comparison of 70% training 30% testing, 80% training 20% testing, and 90% training 10% testing. The results of this study using original data have better results compared to data using HE preprocessing on batch size 16 testing and split data 90% training 10% testing.

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High-performance visual geometric group deep learning architectures for MRI brain tumor classification

Cited by 10 publications

References 21 publications

Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron

Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron

An Automatic Detection of Brain Tumor using CNN & VGG19

Penerapan Deep Learning Menggunakan VGG-16 untuk Klasifikasi Citra Glioma

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