Brain Tumor Classification using Adaptive Neuro-Fuzzy Inference System from MRI

2016 ◽  
Vol 8 (3) ◽  
pp. 203-218 ◽  
Author(s):  
Sudipta Roy ◽  
Shayak Sadhu ◽  
Samir Kumar Bandyopadhyay ◽  
Debnath Bhattacharyya ◽  
Tai-Hoon Kim
Keyword(s):  
Brain Tumor ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Tumor Classification ◽  
Inference System ◽  
Neuro Fuzzy

An Efficient Framework for the Segmentation of Glioma Brain Tumor Using Image Fusion and Co-Active Adaptive Neuro Fuzzy Inference System Classification Method

2021 ◽  
Vol 11 (12) ◽  
pp. 3133-3140
Author(s):  
C. Moorthy ◽  
K. R. Aravind Britto
Keyword(s):  
Brain Tumor ◽  
Image Fusion ◽  
Fuzzy Inference System ◽  
Detection Rate ◽  
Fuzzy Inference ◽  
Morphological Operations ◽  
Inference System ◽  
Pixel Intensity ◽  
Brain Image ◽  
Neuro Fuzzy

The image segmentation of any irregular pixels in Glioma brain image can be considered as difficult. There is a smaller difference between the pixel intensity of both tumor and non-tumor images. The proposed method stated that Glioma brain tumor is detected in brain MRI image by utilizing image fusion based Co-Active Adaptive Neuro Fuzzy Inference System (CANFIS) categorization technique. The low resolution brain image pixels are improved by contrast through image fusion method. This paper uses two different wavelet transforms such as, Discrete and Stationary for fusing two brain images for enhancing the internal regions. The pixels in contrast enhanced image is transformed into multi scale, multi frequency and orientation format through Gabor transform approach. The linear features can be obtained from this Gabor transformed brain image and it is being used to distinguish the non-tumor Glioma brain image from the tumor affected brain image through CANFIS method in this paper. The feature extraction and its impacts are being assigned on the proposed Glioma detection method is also examined in terms of detection rate. Then, morphological operations are involved on the resultant of classified Glioma brain image used to address and segment the tumor portions. The proposed system performance is analyzed with respect to various segmentation approaches. The proposed work simulation results can be compared with different state-of-the art techniques with respect to various parameter metrics and detection rate.

scholarly journals Adaptive Neuro Fuzzy Inference System Used to classify the Five Major Types of Brain Tumors

2020 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Shawni Dutta
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Brain Lesion ◽  
Brain Diseases ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Accurate Treatment ◽  
Life Threatening

Abstract Life threatening diseases in both male and female are Brain tumor, stroke, hemorrhage and multiple sclerosis (MS). The most common and widespread disease among these brain diseases is Brain tumor. Early and accurate diagnosis of brain lesion is vital for determining accurate treatment and prognosis. However, the diagnosis is a very challenging task and can only be performed by specialists in neuroradiology. In this paper, initially MRI image is taken as input and is normalized. The second stage includes extraction of feature vectors from the image which results in reducing redundancy of data to serve as the input to the classifier. The classifier extracted vector as features to produce classified output. The methodology performed very efficiently and accurately. Proposed work exhibits the application of Fuzzy Inference System (FIS) based classifier known as Adaptive Neuro Fuzzy Inference System (ANFIS) to successfully classify the five major types of brain tumors.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

PENGEMBANGAN SISTEM MANAJEMEN PENDUKUNG KEPUTUSAN PENILAIAN MUTU KEPEGAWAIAN DINAS PENDIDIKAN REMBANG MENGGUNAKAN ALGORITMA ANFIS (ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM)

2020 ◽  
Vol 12 (1) ◽  
pp. 6-17
Author(s):  
Angga debby frayudha ◽  
Aris Yulianto ◽  
Fatmawatul Qomariyah
Keyword(s):  
Fuzzy Inference System ◽  
Industry 4.0 ◽  
Fuzzy Inference ◽  
System Model ◽  
Inference System ◽  
Neuro Fuzzy

Di era revolusi industry 4.0 terdapat banyak sekali kemudahan yang diberikan teknologi kepada manusia. Tentu ini akan menjadi baik apabila manusia mampu memanfaatkan hal tersebut dengan baik pula. Namun disisi lain juga bisa mengakibatkan dampak negative terhadap manusia, misalnya dengan adanya internet bisa mengakibatkan manusia melakukan penipuan di media social. Selain itu dengan canggihnya teknologi dapat menjadikan manusia menjadi malas yang bisa berimbas menurunnya kualitas sumber daya manusia. Maka dari itu untuk menghadapi hal ini perlu menyiapkan pendidikan yang baik.Pendidikan akan berjalan baik apabila lembaga yang mengurusnya berkompeten dalam melakukan tugasnya .Penulis coba memberikan ide untuk memprediksi kinerja pegawai Dinas Pendidikan Kabupaten Rembang menggunakan mentode ANFIS (Adaptive Neuro Fuzzy Inference System) guna untuk membantu lembaga tersebut menyeleksi maupun menilai kinerja karyawan demi meningkatkan kualitas dari segi sumber daya manusia. ANFIS merupakan jaringan adaptif yang berbasis pada sistem kesimpulan fuzzy (fuzzy inference system). Model penilaian kinerja pegawai di Dinas Pendidikan Kabupaten Rembang dengan menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) menghasilkan penilaian  yang lebih baik dan akurat.  Hasil pengujian metode tersebut memiliki nilai akurasi 65%. Dengan metode ANFIS (Adaptive Neuro Fuzzy Inference System) dapat memprediksi kinerja karyawan sebagai salah satu pengambilan keputusan terhadap kinerja pegawai. Selain itu nantinya system penlaian kinerja pegawai akan lebih tertata dan efisien.

scholarly journals Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 286 ◽  
Author(s):  
Athanasios Bogiatzis ◽  
Basil Papadopoulos
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Initial Step ◽  
Local Area ◽  
Mathematical Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Complex Procedure ◽  
Artificial Neural Network Ann

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

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