Development of a Low-Cost Eye Screening Tool for Early Detection of Diabetic Retinopathy using Deep Neural Network

It has been said that technology used in the lab does not directly transfer to what is done in healthcare. Research on the use of Artificial Intelligence (AI) in the diagnosis of Diabetic Retinopathy (DR) has seen tremendous growth over the last couple of years but it is also true not much of that knowledge has been transferred into practice to benefit patients in need. One reason is that it’s a new frontier with untested technologies and one that is evolving too fast. Also, the Real Healthcare situation can be very complicated presenting itself with numerous challenges starting with strict regulations to variability in populations. A solution that is implementable needs to address all these concerns including ethics, standards, and any security concerns. It is also important to note that, the current state of AI is specialized to only narrow applications and may not scale when presented with problems of varied nature. A case in point is a patient having DR may be suffering from other ailments such as Glaucoma or cataracts. DR has been a leading cause of blindness for millions of people worldwide, hard to detect when it’s treatable and therefore early eye screening is the solution. In this Capstone project, we seek to integrate Artificial Intelligence with other technologies to deliver a low-cost diagnosis to Diabetic Retinopathy at the same time trying to overcome previous impediments to the implementation of mass eye screening

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions

Penerapa Fuzzy Learning Vector Quantization pada Tingkat Keparahan Macula Edema Berdasarkan Citra Retina Mata

Diabetes Macula Edema (DME) merupakan jenis penyakit gangguan penglihatan akibat dari diabetik retinopati stadium lanjut. Penyakit Diabetik Macula Edema mempengaruhi penglihatan pasien yang dapat menyebabkan kebutaan. Secara global, 21 juta orang diidentifikasi dengan DME dan tingkat prevalensi adalah 10,2%(Panozzo et al., 2004). Beberapa dokter spesialis mata melakukan pengamatan citra retina yang diambil dari hasil menggunakan kamera fendus dan mengelompokan jenis-jenis penyakit macula edema. Berdasarkan uraian masalah yang telah dijelaskan diatas dan peningkatan kasus macula edema diseluruh dunia dan diIndonesia, maka dibuat sebuah penelitian yang menggunakan pengolahan citra digital dan jaringan saraf tiruan. penelitian ini menggunakan metode Hue Saturation Value (HSV) untuk cirri warna, metode Local Binary Pattens (LBP) untuk ciri tekstur dan penelitian menggunakan metode Fuzzy Learning Vektor Quantization (FLVQ) klasifikasi data latih dan data uji,. Jumlah data yang diguakan yaitu 210 data dengan ukuran data 2304x 1536 dan pembagian data menggunakan kfold , learning rate minimal alfa (min α) 0,000001, nilai alfa (α) 0,02 , nilai pengurangan alfa 0,9 , nilai koefisien beta pelebaran (β1) 1,4 dan nilai koefisien beta penyempitan (β2) 0,8. Hasil dari penelitian yang menggunakan data citra retina mata mendapatkan akurasi tertinggi 76,5%. Sehingga dari penelitian yang dilakukan dapat disimpulkan bahwa metode yang digunakan dapat diterapkan pada klasifikasi Fuzzy Learning Vector Quantization. Kata Kunci: Diabetes Macula Edema, Fuzzy Learning Vector Quantization, Hue Saturation Value,Local Binary Pattens, K-fold

Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017

The Computers in Biology and Medicine (CBM) journal promotes the use of com-puting machinery in the fields of bioscience and medicine. Since the first volume in 1970, the importance of computers in these fields has grown dramatically, this is evident in the diversification of topics and an increase in the publication rate. In this study, we quantify both change and diversification of topics covered in CBM. This is done by analysing the author supplied keywords, since they were electronically captured in 1990. The analysis starts by selecting 40 keywords, related to Medical (M) (7), Data (D)(10), Feature (F) (17) and Artificial Intelligence (AI) (6) methods. Automated keyword clustering shows the statistical connection between the selected keywords. We found that the three most popular topics in CBM are: Support Vector Machine (SVM), Elec-troencephalography (EEG) and IMAGE PROCESSING. In a separate analysis step, we bagged the selected keywords into sequential one year time slices and calculated the normalized appearance. The results were visualised with graphs that indicate the CBM topic changes. These graphs show that there was a transition from Artificial Neural Network (ANN) to SVM. In 2006 SVM replaced ANN as the most important AI algo-rithm. Our investigation helps the editorial board to manage and embrace topic change. Furthermore, our analysis is interesting for the general reader, as the results can help them to adjust their research directions

A fast iris recognition system through optimum feature extraction

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person’s lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris template classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique