Perbandingan Metode Deep Learning dalam Mengklasifikasi Citra Scan MRI Penyakit Otak Parkinson

Penyakit Parkinson merupakan gangguan neurodegenerative yang bersifat progresif dan relative umum pada system saraf pusat yang menyebabkan kesulitan dalam bergerak. Biasanya penyakit ini sering terjadi pada individu berusia lebih dari 60 tahun dipengaruhi oleh factor genetic dan lingkungan. Deteksi dini pada penyakit Parkinson dapat mencegah gejala hingga usia tertentu sehingga meningkatkan harapan hidup. Dalam penelitian ini bertujuan untuk menggunakan gambar otak dari Magnetic Resonace Imaging (MRI) untuk mengetahui bagaimana penyakit tersebut menyebar, dengan menggunakan metode deep learning menggunakan model atau arsitektur InceptionV3, VGG16, VGG19, NasnetMobile, dan MobileNet dengan melalui proses Input data - augmentasi - preprocessing - Classification (model a b c d ) - result dan pembelajaran mesin pada kumpulan data klinis dan paraklinis untuk mendiagnosis secara akurat meggunakan dataset yang berasal dari Parkinsons Brain MRI sebanyak 2 kelas yaitu kelas normal dan Parkinson. Hasil dari penelitian menggunakan deep learning berdasarkan kelima algoritma yang digunakan tersebut diperoleh nilai akurasi terbaik dari seluruh model arsitektur adalah arsitektur MobileNet sebesar 99,75% dengan kappa score 99,30% dengan total durasi komputasi selama 2 jam satu meni

GLAUCOMA CLASSIFICATION BASED ON FUNDUS IMAGES PROCESSING WITH CONVOLUTIONAL NEURAL NETWORK

Glaucoma is an eye disease that causes damage to the optic nerve due to increased pressure in the eyeball. Delay in diagnosis and treatment of optic nerve damage due to glaucoma can lead to permanent blindness. Thus, several studies have developed a glaucoma early detection system based on digital image processing and machine learning. This study carried out glaucoma classification based on fundus image processing using Convolutional Neural Network (CNN). The CNN architecture proposed in this study consists of three convolutional layers with output channels 8, 16, 32 sequentially and a filter size of 5×5 at each layer, followed by a pooling layer and a dropout layer at the feature extraction stage. Furthermore, a fully connected layer and softmax activation function was implemented at the classification stage to classify fundus images into normal conditions, early glaucoma, moderate glaucoma, deep glaucoma, and ocular hypertension (OHT). The total amount of fundus image data used in this study consisted of 2000 fundus images divided into 1280 training data, 320 validation data, and 400 test data. 5-fold cross-validation is implemented in the training phase to select the best model. At the testing stage, the best accuracy generated by 99%, with the precision value, recall, f-1 scores and the AUC score are close to 1. According to the system performance results obtained, the proposed model can be used as a tool for medical personnel in classifying glaucoma conditions to provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma

DEVELOPING A REAL TIME ALGORITHM FOR DIAGNOSING GLAUCOMA

A Glaucoma is a group of eye diseases causing optic nerve damage and if not detected at an early stage it may cause permanent blindness. Glaucoma progression precedes some structural damage to the retina are the symptoms of Glaucoma. Manually, it is diagnosed by examination of size, structure, shape, and color of optic disc and optic cup and retinal nerve fiber layer (RNFL), which suffer from the subjectivity of human due to experience, fatigue factor etc., and with the widespread of higher quality medical imaging techniques, there are increasing demands for computer-aided diagnosis (CAD) systems for glaucoma detection, because the human mistakes, other retinal diseases like Age-related Macular Degeneration (AMD) affecting in early glaucoma detection, and the existing medical devices like Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) are expensive. This paper proposes a novel algorithm by extract 13 shape features from disc and cup, extract 25 texture features from RNFL(retinal nerve fiber layer) using gray level co-occurrence method and Tamara algorithm and 3 color features for each of disc and cup and RNFL. Next, best features selected using two methods, first method is the student t-test and the second method applied was the Sequential Feature Selection (SFS) to introduce the best 6 features. The evaluation of proposed algorithm is performed using a RIM_ONE and DRISHTI-GS databases, the average accuracy 97%, maximize area under curve (AUC) 0.99, specificity 96.6% and sensitivity 98.4% using support vector machine classifier (SVM). Future works suggested to design a complete, automated system not just diagnose glaucoma but calculate the progress of the disease too

IMPROVED AUTOMATIC DETECTION OF GLAUCOMA USING CUP-TO-DISK RATIO AND HYBRID CLASSIFIERS.

Glaucoma is one of the most complicated disorder in human eye that causes permanent vision loss gradually if not detect in early stage. It can damage the optic nerve without any symptoms and warnings. Different automated glaucoma detection systems were developed for analyzing glaucoma at early stage but lacked good accuracy of detection. This paper proposes a novel automated glaucoma detection system which effectively process with digital colour fundus images using hybrid classifiers. The proposed system concentrates on both Cup-to Disk Ratio (CDR) and different features to improve the accuracy of glaucoma. Morphological Hough Transform Algorithm (MHTA) is designed for optic disc segmentation. Intensity based elliptic curve method is used for separation of optic cup effectively. Further feature extraction and CDR value can be estimated. Finally, classification is performed with combination of Naive Bayes Classifier and K Nearest Neighbour (KNN). The proposed system is evaluated by using High Resolution Fundus (HRF) database which outperforms the earlier methods in literature in various performance metrics

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classiffication of images in glaucomatous or not. We obtained results of 93% accuracy