Multimodal-based Pediatric Diseases Diagnosis / Asma Nedjem

Public ISBD Titre : Multimodal-based Pediatric Diseases Diagnosis Type de document : document multimédia Auteurs : Asma Nedjem, Auteur ; Leila Benarous, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2024 Importance : 52 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Specialization : Information System and Decision Langues : Anglais Mots-clés : Pediatric diseases classification  computer vision  ResNet50  LSTM  Multimodal Learning  Models Fusion  android mobile app Résumé : In our project, we aimed to solve the pediatric diseases delayed or mistaken diagnosis cases. For this prototype, we concentrated on only five pediatric diseases classification as a proof of concept. The selected have confusing visual symptoms, they are Chickenpox, Kawasaki, Measles, Roseola and Scarlet fever. For the classification, we use both the images capturing the visual symptoms and textual symptoms representing the internal and external state of the patient. The image dataset was collected from public repositories, while the textual dataset was synthetically constructed. For images disease classification we used Residual Network (ResNet50) and for text disease classification we used Long Short-Term Memory (LSTM). The purpose behind using two models was to create one model by fusing them that can classify diseases based on two types of information. The results for the trained models ResNET50, LSTM and their fusion were good (over 90% of accuracy). Our final application is an android mobile application that uses the fused model to allow doctors to do prompt accurate diagnosis of pediatric diseases. note de thèses : Mémoire de master en informatique Multimodal-based Pediatric Diseases Diagnosis [document multimédia] / Asma Nedjem, Auteur ; Leila Benarous, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2024 . - 52 p. + 1 disque optique numérique (CD-ROM). Specialization : Information System and Decision Langues : Anglais Mots-clés : Pediatric diseases classification  computer vision  ResNet50  LSTM  Multimodal Learning  Models Fusion  android mobile app Résumé : In our project, we aimed to solve the pediatric diseases delayed or mistaken diagnosis cases. For this prototype, we concentrated on only five pediatric diseases classification as a proof of concept. The selected have confusing visual symptoms, they are Chickenpox, Kawasaki, Measles, Roseola and Scarlet fever. For the classification, we use both the images capturing the visual symptoms and textual symptoms representing the internal and external state of the patient. The image dataset was collected from public repositories, while the textual dataset was synthetically constructed. For images disease classification we used Residual Network (ResNet50) and for text disease classification we used Long Short-Term Memory (LSTM). The purpose behind using two models was to create one model by fusing them that can classify diseases based on two types of information. The results for the trained models ResNET50, LSTM and their fusion were good (over 90% of accuracy). Our final application is an android mobile application that uses the fused model to allow doctors to do prompt accurate diagnosis of pediatric diseases. note de thèses : Mémoire de master en informatique

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
MF 02-71	MF 02-71	CD	BIBLIOTHEQUE DE FACULTE DES SCIENCES	théses (sci)	Disponible

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