Designing a Pipeline for Predicting Hypothyroidism with Different Machine Learning Classifiers

Prusty, Sashikanta; Patnaik, Srikanta; Dash, Sujit Kumar; Prusty, Sushree Gayatri Priyadarsini

doi:10.1109/odicon54453.2022.10010212

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Secondly, efficient models such as MobileNetV3, MnasNet, and EfficientNetB0 were incorporated to ensure fast and resource-efficient predictions, making the proposed system practical for real-world clinical applications. Furthermore, these models have proven successful across a wide range of computer vision applications, as substantiated in previous works [26][27][28][29][30][31][32].…”

Section: Model Architecturesmentioning

confidence: 67%

Evaluation of atopic dermatitis severity using artificial intelligence

Maulana,

Noviandy,

Suhendra

et al. 2023

Narra J

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Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pre-trained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.

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Section: Model Architecturesmentioning

confidence: 67%

Evaluation of atopic dermatitis severity using artificial intelligence

Maulana,

Noviandy,

Suhendra

et al. 2023

Narra J

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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: 99%

“…Other studies are, far from exhaustive ul Haq et al ( 2021 My focus herein is pneumonia detection using X-ray, therefore, pneumonia works are more related to our endeavour. Prusty et al (2022) used a ResNet50V2 instead of the classic MobileNet, that I am using herein, and also did our main reference Kermany et al (2018). ResNet50V2 and MobileNet are both convolutional neural networks (CNNs) that are widely used in computer vision tasks.…”

Section: Transfer Learning Applied To Chess X-ray Imagesmentioning

confidence: 99%

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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