(deposit) Tb Diagnosis System Based On Symptoms And X-ray Images Analysis With Rule-based System And Dense Convolutional Network (densenet) Methods

Suharman, Dedi (2022) (deposit) Tb Diagnosis System Based On Symptoms And X-ray Images Analysis With Rule-based System And Dense Convolutional Network (densenet) Methods. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Health is the most important thing. However, most people do not realize that cough can be a symptom of Tuberculosis which is also known as TB, the symptoms of Tuberculosis must be treated immediately by a pulmonary specialist. However, the distribution of lung specialists has not been comprehensive, which has resulted in some districts not having lung specialists. Based on this discussion, a study was carried out on the diagnosis system of Tuberculosis so that it could determine the similarity of the patient's lung condition with the symptoms of Tuberculosis. There are several stages in the process of diagnosing tuberculosis. The first step is to fill in several expert system rules related to the symptoms of Tuberculosis and assisted with X-ray image input, after that the input that has been entered consists of the Rule Based System and X-ray image input. Furthermore, each input is carried out in a different process, for the Rule Based System it is processed with Certainty Factor and assisted by the Forward Chaining inference engine. Meanwhile, the X-ray images were processed using the Convolutional Network method with the Pretrained DenseNet201 model, the model being trained was tested such as the Confusion Matrix, classification testing with X-ray images and the experimental results obtained an accuracy of 99.8%. Keywords : Tuberculosis, Convolutional Network, X-ray, System Expert

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Tuberkulosis, Jaringan Konvolusi, Gambar X-ray, Sistem Pakar
Subjects: Q Science > Q Science (General)
R Medicine > R Medicine (General)
T Technology > T Technology (General)
Divisions: Fakultas Teknik > Program Studi Teknik Informatika
Depositing User: 1461800012 Dedi Suharman
Date Deposited: 20 Sep 2022 03:31
Last Modified: 08 Nov 2022 02:13
URI: http://repository.untag-sby.ac.id/id/eprint/19875

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