A Computer-aided-Diagnosis of Tonsillitis Using Tonsi size and Color

Phensadsaeng, Pranithan; Kumhom, Pinit; Chamnongthai, Kosin

doi:10.1109/iscas.2006.1693895

Cited by 7 publications

(8 citation statements)

References 3 publications

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“…This method is said to give 80% accuracy for tonsillitis detection. Using a green channel image for tonsil extraction, window buffer, mean value calculation, and applying a Sobel edge detector can also be used [11]. In this method, the RGB image and tonsil extracted area are input to extract red color intensity and tonsil area in pixels.…”

Section: Related Workmentioning

confidence: 99%

Tonsillitis based Early Diagnosis of COVID-19 for Mass-Screening using One-Shot Learning Framework

Mulchandani

Dalal

Ramwala

et al. 2020

2020 IEEE 17th India Council International Conference (INDICON)

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The outbreak of COVID-19 received substantial international attention due to its life-threatening repercussions. Automatic and non-invasive mass screening is pivotal to ensure that the suspected cases can be quarantined, and the spread of the disease can be controlled during the incubation period of the coronavirus. Currently, thermal screening is the only non-invasive mass-screening technique being used. However, with the consumption of paracetamol, the symptoms of fever can be suppressed. Throat inflammation is one of the perceivable symptoms of COVID-19. This paper proposes a novel approach of analyzing the throat images using a Siamese Network based One-Shot learning framework for the identification of Tonsillitis and Pharyngitis for the automatic mass-screening and early-detection of COVID-19 that can be administered without direct contact with the infectious patients, thereby reducing the burden on the medical and paramedical fraternity. Quantitative and qualitative evaluation exhibit promising results and indicate the reliability of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Tonsillitis based Early Diagnosis of COVID-19 for Mass-Screening using One-Shot Learning Framework

Mulchandani

Dalal

Ramwala

et al. 2020

2020 IEEE 17th India Council International Conference (INDICON)

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“…Gbr. 6 Contoh tonsil normal terdeteksi tonsiliti Gbr.7 Contoh tonsilitis terdeteksi tonsil normal Hasil yang didapat dari percobaan di atas menghasilkan akurasi yang lebih tinggi dari penelitian sebelumnya [4] [6]. Dengan metode yang lebih sederhana dibandingkan dengan metode transformasi maupun metode klasifikasi dari penelitian sebelumnya, terbukti metode yang diusulkan menghasilkan hasil yang lebih baik.…”

Section: Gbr 3 Sistem Perekaman Dan Pendeteksian Tonsilitisunclassified

“…Masyarakat masih sangat awam mengenai penyakit ini, padahal tonsilitis merupakan salah satu penyebab utama serangan jantung dan pneumonia [4]. Dan diagnosis yang dilakukan oleh dokter saat ini masih dilakukan dengan cara langsung mengecek pada rongga mulut pasiennya, padahal saat menderita tonsilitis pasien akan merasa sangat kesakitan apabila diminta untuk membuka rongga mulut, terlebih lagi dengan waktu yang cukup lama [2].…”

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“…Beberapa penelitian pendeteksian tonsilitis dengan memanfaatkan pengolahan citra digital telah dilakukan. Deteksi tonsilitisnya menggunakan metode transformasi fourier 2D dan ellips hough transform sebagai ekstraksi ciri serta metode fuzzy logic dan neural network sebagai metode klasifikasinya [4] [6]. Pada penelitian tersebut, dilakukan banyak transformasi untuk mengolah citra tonsil.…”

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Simulasi Deteksi Tonsilitis Mengunakan Pengolahan Citra Digital Berdasarkan Warna dan Luasan pada Tonsil

Prasetya¹,

Rizal²,

Ramatryana³

2015

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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Image retrieval process of fruits and flowers with CBIR concept was represented by the colors and shapes using adaptive histogram method for color, and invariant moment for shape. To measure the similarity between the query image and the basis data image Euclidean distance function was used, where the result is f (x). Calculations for f (y) through the process of 'fuzzy-ing'-S curve, where the value of f (x) guides the sigmoid function. The value f(y) on each image than the threshold value based image query. Basically, the algorithm displays the image based on Threshold features, by comparing the threshold value with the value f(y). A high grade value (approaching 1) indicates that the feature of the sample (query) image is similar to the basis data image, and vice versa. The process was continued by comparing the value grades of the image representation of color and form using min operator in fuzzy logic, so that it only displayed several images that have some resemblances in accordance with the original image. The advantage of threshold algorithm and the fuzzy function-compared to other methods-lies in the simplicity method in the image retrieval, so that the performance of CBIR becomes more reliable and effective. ABSTRAK Proses temu kembali citra buah dan bunga dengan konsep CBIR direpresentasi dengan warna dan bentuk mengunakan metode Adaptive Histogram untuk warna, dan invariant moment untuk bentuk. Untuk mengukur kemiripan antara citra query dan citra basis data digunakan fungsi jarak Euclid, di mana hasilnya sebagai f(x). Perhitungan untuk f (y) melalui proses fuzzy-fikasi pada kurva-S, di mana nilai f(x) menjadi pedoman dalam menjalankan fungsi sigmoid. Nilai f(y) pada setiap citra dibandingkan berdasarkan nilai ambang citra query. Pada dasarnya, algoritma Threshold menampilkan citra berdasarkan cirinya, dengan membandingkan nilai ambang dengan nilai f(y). Nilai grade tinggi (mendekati 1) menunjukkan bahwa ciri citra contoh (query) mirip dengan citra basis data, begitu juga sebaliknya. Proses dilanjutkan dengan membandingkan nilai grade representasi citra warna dan bentuk mengunakan operator min pada logika fuzzy, sehingga ditampilkan beberapa citra saja yang mempunyai kemiripan sesuai dengan citra asli. Kelebihan algoritma Threshold dan fungsi fuzzy ini, dibandingkan dengan metode lainnya terletak pada simplisitas metode dalam temu kembali citra, sehingga kinerja CBIR menjadi lebih andal dan efektif.

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“…To support the doctor's practices, it has taken pictures in the oral cavity to see a feature of tonsil grand for instead of checking directly in the oral cavity. In open literature, only two related papers have involved with finding out the tonsil using images [2], [3]. Such two papers have checked a size of tonsil grand using the ratio of the size of usual tonsil grand and new tonsil grand.…”

Section: Introductionmentioning

confidence: 99%

A method of detecting tonsillitis images based on medical knowledge and neural network

Jirawanitcharoen

Kiattisin

Leelasantitham

et al. 2009

2009 2nd IEEE International Conference on Computer Science and Information Technology

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Tonsillitis is a disease occurring mostly in child and adults as this disease may take to the other effects. Nowadays, a detection of tonsil grand exploits medical doctor's diagnosis to check on oral cavity. Therefore, this paper presents a method of detecting tonsillitis images based on medical knowledge and neural network (NN); as well as, the paper considers three important factors which can be indicated in swelling by the pictures in terms of a) the ratio of tonsil grand dimension, b) average of tonsil grand color and c) surface of tonsil grand as it is purulent (yes/no) using two dimensional Fast Fourier Transform (2D FFT). Finally, the three factors are inputted into NN, and samples of 30 pictures are used for training into the NN which is divided by tonsillitis patience 15 pictures and usual tonsil grand 15 pictures. In the experimental results, 20 pictures are tested to compare with the result of the medical doctor's demonstration as the result of correction approximately at 90%.

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A Computer-aided-Diagnosis of Tonsillitis Using Tonsi size and Color

Cited by 7 publications

References 3 publications

Tonsillitis based Early Diagnosis of COVID-19 for Mass-Screening using One-Shot Learning Framework

Tonsillitis based Early Diagnosis of COVID-19 for Mass-Screening using One-Shot Learning Framework

Simulasi Deteksi Tonsilitis Mengunakan Pengolahan Citra Digital Berdasarkan Warna dan Luasan pada Tonsil

A method of detecting tonsillitis images based on medical knowledge and neural network

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