Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

Jawahar, Malathy; Anbarasi, L. Jani; Jayachandran, Prassanna; Ramachandran, Manikandan; Al‐Turjman, Fadi

doi:10.4018/ijehmc.20220701.oa2

Cited by 4 publications

(5 citation statements)

References 32 publications

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“…These studies demonstrate the effectiveness of transfer learning in improving the diagnosis of chess X-ray images. Other studies are, far from exhaustive ul Haq et al (2021); Hamida et al (2021); Duong et al (2021); Prusty et al (2022); Minaee et al (2020); Fraiwan, Al-Kofahi, Ibnian and Hanatleh (2022); Jawahar et al (2022); Ohata et al (2021); Apostolopoulos and Bessiana (2020).…”

Section: Literature Reviewmentioning

confidence: 98%

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Robodoc: a conversational-AI based app for medical conversations

Guerra Pires

2024

Preprint

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Artificial Intelligence (AI) evolved in trends. Currently, the trend is Conversational Artificial Intelligence (CAI). Those models of AI are focused on text-related tasks, and their commonest applications are chatbots. On this paper, we explore a smart chatbot using the Large Language Models (LLMs) from openAI. I have used a tool called Teachable Machine (TM) from Google to apply transfer learning and create image-based models. I have built two image-based model: for X-ray and for OCT. The model of X-ray is able to detect viral and bacterial pneumonia, whereas the Optical coherence tomography (OCT) model can detect Drusen, Choroidal Neovascularization (CNV) and (Diabetic Macular Edema DME) conditions on the patient’s eyes image. I have also used TensorFlow.js from Google to create a diabetes detection model. All those models are integrated into a chatbot, that according to the message entered by a user, is able to use the models intelligently. Our results show a good integration between the models and the chatbot, with slight deviations from the expected behaviors. For the OCT model, we have also tested a stub function for medical appointments done by the bot, based on how serious is the patient condition. The future of artificial intelligence are public APIs, as I have shown that a complex model can be built, without a complex research infrastructure, and with low costs. Bioinformatics may have gained a new supporter towards more friendly interfaces on bioinformatics.

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Section: Literature Reviewmentioning

confidence: 98%

“…Jawahar et al (2022) is concerned about diagnosing COVID-19 pneumonia using patients’ chest X-Ray images. They stress how new, and important is this application.…”

Section: Literature Reviewmentioning

confidence: 99%

Robodoc: a conversational-AI based app for medical conversations

Guerra Pires

2024

Preprint

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“…Jawahar, M., et al. [5] reviews the different methods of predicting and diagnosing COVID-19. In this study, data related to single voice [7] , lung ultrasound, Computerized Tomography (C.T.…”

Section: Background and Related Workmentioning

confidence: 99%

“…There are currently several ways to diagnose COVID-19 disease. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) testing [4] and methods based on radiographic images [5] are examples of these ways. Problems such as errors in results, high cost of test kits, and lack of advanced medical imaging tools and related specialists [4] , [6] have led researchers to consider the use of low-cost, affordable Chest X-ray (CXR) images [7] , [8] , [9] , [10] , [11] , [12] .…”

Section: Introductionmentioning

confidence: 99%

A fuzzy fine-tuned model for COVID-19 diagnosis

Esmi¹,

Golshan²,

Asadi³

et al. 2023

Computers in Biology and Medicine

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“…Such data sets include information on COVID-19 diagnosis, case reporting, transmission estimation, sentiment analysis from social media, and semantic analysis from the collection of scholarly articles. Most of the COVID-19 diagnosis data are based on image (CT scans, X-ray images) analysis [20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data

et al. 2021

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New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when medical knowledge is not complete and the symptoms are ambiguous. The use of machine learning tools can help to filter out those sick patients who do not need to be tested for spreading the pathogen, especially in the event of an overwhelming increase in disease transmission. This work presents a screening support system that can precisely identify patients who do not carry the disease. The decision of the system is made on the basis of patient survey data that are easy to collect. A case study on a data set of symptomatic COVID-19 patients shows that the system can be effective in the initial phase of the epidemic. The case study presents an analysis of two classifiers that were tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models is presented. The explanation enables the users to understand the basis of the decision made by the model. The obtained classification models provide the basis for the DECODE service, which could serve as support in screening patients with COVID-19 disease at the initial stage of the pandemic. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set, consisting of more than 3000 examples, is based on questionnaires collected at a hospital in Poland.

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Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

Cited by 4 publications

References 32 publications

Robodoc: a conversational-AI based app for medical conversations

Robodoc: a conversational-AI based app for medical conversations

A fuzzy fine-tuned model for COVID-19 diagnosis

Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data

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