Towards Development of a Computerised System for Screening and Monitoring of Diabetic Retinopathy

One of the complications of diabetes can lead to vision problems when it occurs in the retina which is known as diabetic retinopathy (DR). In practice, to diagnose and monitor DR severity, ophthalmologists observe the presence of several pathologies in colour retinal fundus images. However, this approach is tedious and time consuming, especially in the case of screening for early detection. Several techniques have been developed to achieve the final goal that is an automated DR screening system. This paper presents three kinds of approach towards the development of a computerised DR screening and monitoring system. The first approach is pathology-based methods. This approach detects and analyses several pathologies such as microaneurysms, haemorrhages, exudates and changes of retinal vessels. This approach achieves the performance results of more than 90% of accuracy, sensitivity and specificity for detection of the pathologies. The second approach is retinal structure-based methods. This approach detects optic disc, macula and foveal avascular zone (FAZ). The FAZ determination successfully achieves the accuracy of around 97%. DR severity has been proven to have strong correlation up to 0.912 with the enlargement of FAZ. The third approach is deep learning-based methods. This approach has achieved promising results with accuracy of more than 95% in screening and grading the severity of the DR. The third approach offers several advantages compared to the two previous ones in which this approach does not need to specifically detect the presence of pathologies nor the retinal structure to determine DR grade. However, this approach needs huge dataset to learn. The next development is to implement the deep learning based method into a low-cost embedded system

NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system

NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system

Systematic literature review of dermoscopic pigmented skin lesions classification using convolutional neural network (CNN)

The occurrence of pigmented skin lesions (PSL), including melanoma, are rising, and early detection is crucial for reducing mortality. To assist Pigmented skin lesions, including melanoma, are rising, and early detection is crucial in reducing mortality. To aid dermatologists in early detection, computational techniques have been developed. This research conducted a systematic literature review (SLR) to identify research goals, datasets, methodologies, and performance evaluation methods used in categorizing dermoscopic lesions. This review focuses on using convolutional neural networks (CNNs) in analyzing PSL. Based on specific inclusion and exclusion criteria, the review included 54 primary studies published on Scopus and PubMed between 2018 and 2022. The results showed that ResNet and self-developed CNN were used in 22% of the studies, followed by Ensemble at 20% and DenseNet at 9%. Public datasets such as ISIC 2019 were predominantly used, and 85% of the classifiers used were softmax. The findings suggest that the input, architecture, and output/feature modifications can enhance the model's performance, although improving sensitivity in multiclass classification remains a challenge. While there is no specific model approach to solve the problem in this area, we recommend simultaneously modifying the three clusters to improve the model's performance

Comparison of multi-distance signal level difference Hjorth descriptor and its variations for lung sound classifications

A biological signal has the multi-scale and signals complexity properties. Many studies have used the signal complexity calculation methods and multi-scale analysis to analyze the biological signal, such as lung sound. Signal complexity methods used in the biological signal analysis include entropy, fractal analysis, and Hjorth descriptor. Meanwhile, the commonly used multi-scale methods include wavelet analysis, coarse-grained procedure, and empirical mode decomposition (EMD). One of the multi-scale methods in the biological signal analysis is the multi-distance signal level difference (MSLD), which calculates a difference between two signal samples at a specific distance. In previous studies, MSLD was combined with Hjorth descriptor for lung sound classification. MSLD has the potential to be developed by modifying the fundamental equation of MSLD. This study presents the comparison of MSLD and its variations combined with Hjorth descriptor for lung sound classification. The results showed that MSLD and its variations had the highest accuracy of 98.99% for five lung sound data classes. The results of this study provided several alternatives for multi-scale signal complexity analysis method for biological signals

Analysis of the Indonesian Vowel /e/ For Lip Synchronization Animation

Currently, voice recognition technology is widely used to produce lip sync animation. Vowels take the most dominant roles for lip sync animation as it always exists in every syllable. Therefore, it is necessary to select appropriate vowel traits for the system to be accurate. In general, there are five vowels of Indonesian language, namely /a/ /i/ /u/ /e/ and /o/. However, there are two vowels that contain several different tones: /o/ that are pronounced /o/ and /O/, and /e/ that are pronounced /e/, /ǝ/, and /ɛ/. The difference in tone can affect the accuracy of voice recognition on the lip sync animation system if it is not specified further. In this paper, the characteristic values of vowel /e/, /ǝ/, and /ɛ/ are compared and analyzed to find the significance of the difference. The sought characteristic values are the frequency of the formant (F1, F2, and F3) through the Praat software used to extract the features. Comparison is done using a statistical test of t-test. The results show that the three vowel tones /e/ have significant differences for all of F1 and most of F

Improving Phoneme to Viseme Mapping for Indonesian Language

The lip synchronization technology of animation can run automatically through the phoneme-to-viseme map. Since the complexity of facial muscles causes the shape of the mouth to vary greatly, phoneme-to-viseme mapping always has challenging problems. One of them is the allophone vowel problem. The resemblance makes many researchers clustering them into one class. This paper discusses the certainty of allophone vowels as a variable of the phoneme-to-viseme map. Vowel allophones pre-processing as a proposed method is carried out through formant frequency feature extraction methods and then compared by t-test to find out the significance of the difference. The results of pre-processing are then used to reference the initial data when building phoneme-to-viseme maps. This research was conducted on maps and allophones of the Indonesian language. Maps that have been built are then compared with other maps using the HMM method in the value of word correctness and accuracy. The results show that viseme mapping preceded by allophonic pre-processing makes map performance more accurate when compared to other maps

A Review of Feature Selection and Classification Approaches for Heart Disease Prediction

Cardiovascular disease has been the number one illness to cause death in the world for years. As information technology develops, many researchers have conducted studies on a computer-assisted diagnosis for heart disease. Predicting heart disease using a computer-assisted system can reduce time and costs. Feature selection can be used to choose the most relevant variables for heart disease. It includes filter, wrapper, embedded, and hybrid. The filter method excels in computation speed. The wrapper and embedded methods consider feature dependencies and interact with classifiers. The hybrid method takes advantage of several methods. Classification is a data mining technique to predict heart disease. It includes traditional machine learning, ensemble learning, hybrid, and deep learning. Traditional machine learning uses a specific algorithm. The ensemble learning combines the predictions of multiple classifiers to improve the performance of a single classifier. The hybrid approach combines some techniques and takes advantage of each method. Deep learning does not require a predetermined feature engineering. This research provides an overview of feature selection and classification methods for the prediction of heart disease in the last ten years. Thus, it can be used as a reference in choosing a method for heart disease prediction for future research

Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning

Tumors are cells that grow abnormally and uncontrollably, whereas brain tumors are abnormally growing cells growing in or near the brain. It is estimated that 23,890 adults (13,590 males and 10,300 females) in the United States and 3,540 children under the age of 15 would be diagnosed with a brain tumor. Meanwhile, there are over 250 cases in Indonesia of patients afflicted with brain tumors, both adults and infants. The doctor or medical personnel usually conducted a radiological test that commonly performed using magnetic resonance image (MRI) to identify the brain tumor. From several studies, each researcher claims that the results of their proposed method can detect brain tumors with high accuracy; however, there are still flaws in their methods. This paper will discuss the classification of MRI-based brain tumors using deep learning and transfer learning. Transfer learning allows for various domains, functions, and distributions used in training and research. This research used a public dataset. The dataset comprises 253 images, divided into 98 tumor-free brain images and 155 tumor images. Residual Network (ResNet), Neural Architecture Search Network (NASNet), Xception, DenseNet, and Visual Geometry Group (VGG) are the techniques that will use in this paper. The results got to show that the ResNet50 model gets 96% for the accuracy, and VGG16 gets 96% for the accuracy. The results obtained indicate that transfer learning can handle medical images