Detection of Pterygium Disease Using Forward Chaining and Viola Jones Algorithm

Hanifah, Umu; Kusuma, Purba Daru; Setianingsih, Casi

doi:10.1109/iccerec.2018.8712102

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…Table 1 [21][22][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] presents 17 papers that use traditional methods to assist in pterygium diagnosis in chronological order, with a summary of each paper. The earliest paper appeared in 2011, in which Papa et al [21] applied their algorithm based on gravitational search and Optimum-Path Forest (OPF) classifier to a binary pterygium dataset, achieving an accuracy of 98.4%.…”

Section: S U M M a Ry O F C O N V E N T I O N A L M E T H O D S Appli...mentioning

confidence: 99%

Artificial intelligence assisted pterygium diagnosis: current status and perspectives

Chen,

Wu,

Zhu

et al. 2023

Int J Ophthalmol

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Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.

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Section: S U M M a Ry O F C O N V E N T I O N A L M E T H O D S Appli...mentioning

confidence: 99%

Artificial intelligence assisted pterygium diagnosis: current status and perspectives

Chen,

Wu,

Zhu

et al. 2023

Int J Ophthalmol

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show abstract

“…The extracted region is labeled as abnormal tissues, which is then used to determine the severity level of pterygium. Another method in [ 15 ] applied a haar-like feature coupled with an AdaBoost classifier to extract pterygium-infected tissues. In [ 16 ], Fourier harmonic analysis was applied by identifying various circular diameters that can fit the infected tissue regions.…”

Section: Related Workmentioning

confidence: 99%

Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation

Abdani

Zulkifley

2021

Diagnostics

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Pterygium is an eye condition that is prevalent among workers that are frequently exposed to sunlight radiation. However, most of them are not aware of this condition, which motivates many volunteers to set up health awareness booths to give them free health screening. As a result, a screening tool that can be operated on various platforms is needed to support the automated pterygium assessment. One of the crucial functions of this assessment is to extract the infected regions, which directly correlates with the severity levels. Hence, Group-PPM-Net is proposed by integrating a spatial pyramid pooling module (PPM) and group convolution to the deep learning segmentation network. The system uses a standard mobile phone camera input, which is then fed to a modified encoder-decoder convolutional neural network, inspired by a Fully Convolutional Dense Network that consists of a total of 11 dense blocks. A PPM is integrated into the network because of its multi-scale capability, which is useful for multi-scale tissue extraction. The shape of the tissues remains relatively constant, but the size will differ according to the severity levels. Moreover, group and shuffle convolution modules are also integrated at the decoder side of Group-PPM-Net by placing them at the starting layer of each dense block. The addition of these modules allows better correlation among the filters in each group, while the shuffle process increases channel variation that the filters can learn from. The results show that the proposed method obtains mean accuracy, mean intersection over union, Hausdorff distance, and Jaccard index performances of 0.9330, 0.8640, 11.5474, and 0.7966, respectively.

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“…Perkembangan teknologi informasi yang sangat pesat saat ini telah dimanfaatkan diberbagai bidang kehidupan seperti pemerintahan, pendidikan, dan bidang kesehatan mental [1]. Memadukan teknlogi informasi dan psikologi dapat mempermudah pakar dalam menjalankan tugasnya dan masyarakat juga lebih mudah dalam memenuhi kebutuhanya terkait dengan permaslahan psikologi [2]. Hal ini yang menjadi dasar dalam penerapan aplikasi sistem pakar untuk mengukur tingkat akurasi dalam mengidentifikasi kepribadian dan karakter menggunakan metode forward chaining [3].…”

Section: Pendahuluanunclassified

Sistem Pakar untuk Mengukur Tingkat Akurasi Dalam Mengidentifikasi Kepribadian Menggunaakan Metode Forward Chaining

Wijaya

Yunus

2020

jidt

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Personality identification is one of the important things to know yourself and others. This identification is carried out based on the trait symptoms a person has based on the big five personality theory. This study aims to measure the level of accuracy in identifying personality based on visible trait symptoms. The data processed in this study were 10 patients. This data comes from psychology experts at the Faculty of Psychology, Riau Islamic University. The method used to process data is forward chaining. Based on the sample data processing that has been done to identify a person's personality and character, it has an accuracy rate of 100%. The results of this study obtained the type of personality which will then be understood by everyone to be able to identify their personality more deeply

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Detection of Pterygium Disease Using Forward Chaining and Viola Jones Algorithm

Cited by 9 publications

References 3 publications

Artificial intelligence assisted pterygium diagnosis: current status and perspectives

Artificial intelligence assisted pterygium diagnosis: current status and perspectives

Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation

Sistem Pakar untuk Mengukur Tingkat Akurasi Dalam Mengidentifikasi Kepribadian Menggunaakan Metode Forward Chaining

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