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Rizal Adi Saputra

Macular edema classification using self-organizing map and generalized learning vector quantization

"Macular edema is a kind of human sight disease as a result of advanced stage of diabetic retinopathy. It affects the central vision of patients and in severe cases lead to blindness. However, it is still difficult to diagnose the grade of macular edema quickly and accurately even by the medical doctor's skill. This paper proposes a new method to classify fundus images of diabetics by combining Self-Organizing Maps (SOM) and Generalized Vector Quantization (GLVQ) that will produce optimal weight in grading macular edema disease class. The proposed method consists of two learning phases. In the first phase, SOM is used to obtain the optimal weight based on dataset and random weight input. The second phase, GLVQ is used as main method to train data based on optimal weight gained from SOM. Final weights from GLVQ are used in fundus image classification. Experimental result shows that the proposed method is good for classification, with accuracy, sensitivity, and specificity at 80%, 100%, and 60%, respectively."

Surabaya: Faculty of Information and Technology, Department of Informatics Institut Teknologi Sepuluh Nopember, 2014

AJ-Pdf

Artikel Jurnal Universitas Indonesia Library

Ely Sudarsono

Pengoptimasian Metode Convolutional Neural Network Dengan Optimasi Lookahead-Diffgrad Untuk Mengklasifikasikan Citra Retina Penyakit Katarak = An Optimized Convolution Neural Network using Lookahead-diffGrad for Cataract Fundus Image Classification

"Indonesia merupakan salah satu negara dengan penduduk terbanyak yang mengalami kebutaan yang disebabkan oleh katarak sebesar 77,7 %. Pendeteksian terhadap pasien katarak dapat dilakukan menggunakan citra fundus dengan metode komputasi. Salah satu metode komputasi populer dalam klasifikasi citra fundus adalah deep learning yang merupakan salah satu pendekatan machine learning. Pada tesis ini, model convolutional neural network (CNN) yang digunakan adalah arsitektur AlexNet dengan Lookahead-diffGrad optimizer. Data yang digunakan dalam penelitian ini diambil dari situs Kaggle yang berisi citra fundus katarak. Selanjutnya, dilakukan tahap pra-pengolahan pada citra seperti menerapkan resize dan menerapkan normalisasi agar semua citra dapat diinput ke dalam model dengan ukuran yang sama serta meningkatkan kinerja model. Hasil penelitian ini menunjukkan CNN dengan Lookahead-diffGrad optimizer pada dataset citra retina katarak dapat mengklasifikasikan data menjadi dua kelas, yaitu normal dan katarak, sehingga dapat membantu untuk mendiagnosis penyakit tersebut dengan baik. Selain itu, hasil terbaik juga diperoleh oleh CNN dengan Lookahead-diffGrad optimizer berdasarkan nilai loss sebesar 0,0010 dan akurasi 100 % dibandingkan berbagai optimizer lainnya untuk mengklasifikasikan dataset citra retina katarak.

Indonesia is one of the countries with the most people experiencing blindness due to cataracts at up to 77.7% of the population. Detection of cataract patients can be done using fundus images with computational methods. One of the popular computational methods in the classification of fundus images is deep learning, which is one of machine learning approaches. In this thesis, the convolutional neural network (CNN) model used is the AlexNet architecture with Lookahead-diffGrad optimizer. The data used in this study were taken from the Kaggle website which contains the images of cataract fundus. Furthermore, the pre-processing stage of the image is carried out such as applying resizing and applying normalization so that all images can be inputted into the model with the same size and improve the performance of the model. The results of this study indicate that CNN using the Lookahead-diffGrad optimizer on the retinal cataract image dataset can classify the data into two classes, namely normal and cataracts, so that it can help diagnose the disease properly. In addition, the best results were obtained by CNN with the Lookahead-diffGrad optimizer based on a loss value of 0.0010 and 100% accuracy compared to other optimizers for classifying the retinal cataract image dataset."

Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2020

T-pdf

UI - Tesis Membership Universitas Indonesia Library

Radifa Hilya Paradisa

Evaluasi kinerja metode convolutional neural networks dalam mendeteksi diabetic retinopathy = Performance evaluation of convolutional neural networks methods in detecting diabetic retinopathy

"ABSTRAK

Diabetic Retinopathy (DR) merupakan komplikasi jangka panjang dari Diabetes Mellitus (DM) yang mempengaruhi penglihatan karena adanya mikrovaskular pada retina. Hal ini dapat mengakibatkan gangguan penglihatan dan kebutaan jika ditangani terlambat. DR dapat dideteksi melalui pemeriksaan citra fundus. Salah satu pendekatan dalam mendeteksi DR pada citra fundus yaitu dengan pendekatan deep learning yang merupakan salah satu metode implementasi dari machine learning. Dalam penelitian ini, digunakan metode Convolutional Neural Networks (CNN) dengan arsitektur ResNet-50 dan DenseNet-121. Data yang digunakan dalam penelitian ini diambil dari DIARETDB1 yang merupakan online database yang berisi gambar fundus. Selanjutnya, dilakukan tahap preprocessing pada citra untuk meningkatkan kinerja model seperti mengambil green channel dan menerapkan inverted green channel, mengubah citra warna menjadi grayscale, dan menerapkan Contrast Limited Adaptive Histogram Equalization (CLAHE) untuk penyeragaman kontras pada citra. Hasil penelitian ini menunjukkan bahwa model ResNet-50 lebih baik dibandingkan DenseNet-121 dalam mendeteksi DR. Hasil terbaik dari beberapa kasus testing model ResNet-50 yaitu accuracy, precision, dan recall masing-masing sebesar 92,2%, 93,6%, dan 92,6% dengan running time untuk training selama 6 menit 21,296 detik dan testing selama 1,174 detik.

ABSTRACT

Diabetic Retinopathy (DR) is a long-term complication of Diabetes Mellitus (DM) that affects vision because of the presence of microvascular retinal. This can result in visual impairment and blindness if treated late. DR can be detected by examining fundus images. One approach to detecting DR in fundus images is the deep learning approach which is one of the methods of implementing machine learning. In this study, the Convolutional Neural Networks (CNN) method is used with the ResNet-50 and DenseNet-121 architectures. The data used in this study were taken from DIARETDB1, which is an online database that contains fundus images. Then, pre-processing stage is carried out on the fundus image to improve model performance such as selected the green channel from the images and inverted it, converted the images into grayscale images, and applied Contrast Limited Adaptive Histogram Equalization (CLAHE) for uniform contrast in the images. The results of this study indicate that the ResNet-50 model is better than DenseNet-121 in detecting DR. The best results from several cases testing the ResNet-50 model are accuracy, precision, and recall of 92.2%, 93.6%, and 92.6% respectively with running time for training for 6 minutes 21.296 seconds and testing for 1.174 seconds."

2019

S-Pdf

UI - Skripsi Membership Universitas Indonesia Library

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