Deep learning-based ai integration in dual mobile applications for Skin disease detection and doctor-patient appointment management / Ahmed Houssam Eddine Taleb

Public ISBD Titre : Deep learning-based ai integration in dual mobile applications for Skin disease detection and doctor-patient appointment management Type de document : document multimédia Auteurs : Ahmed Houssam Eddine Taleb, Auteur ; Mustapha Bouakkaz, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2025 Importance : 44 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option: Data science et artificial intelligence Langues : Anglais (eng) Résumé : Skin diseases range from minor conditions to serious cancers like melanoma, where early detection is crucial for effective treatment and survival. This thesis presents a comprehensive AI-driven system for skin disease classification using the HAM10000 dataset, which includes over 10,000 dermatoscopic images across seven lesion categories. At its core is a specially designed Convolutional Neural Network (CNN) enhanced with residual blocks and attention mechanisms, trained on a Colab Pro A100 GPU. The model outperformed popular pretrained networks—ResNet50, DenseNet121, and EfficientNetB0—achieving a validation accuracy of 85.08, while the best pretrained model reached only 59.63. For practical deployment, the model was converted to TensorFlow Lite and embedded into two cross-platform Flutter Firebase mobile apps: a Patient App for AI-based skin image analysis and appointment booking, and a Doctor App for appointment management and AI-assisted diagnosis. This work delivers an efficient, scalable solution for early skin disease detection and smart healthcare support note de thèses : Mémoire de master en informatiques Deep learning-based ai integration in dual mobile applications for Skin disease detection and doctor-patient appointment management [document multimédia] / Ahmed Houssam Eddine Taleb, Auteur ; Mustapha Bouakkaz, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 44 p. + 1 disque optique numérique (CD-ROM). Option: Data science et artificial intelligence Langues : Anglais (eng) Résumé : Skin diseases range from minor conditions to serious cancers like melanoma, where early detection is crucial for effective treatment and survival. This thesis presents a comprehensive AI-driven system for skin disease classification using the HAM10000 dataset, which includes over 10,000 dermatoscopic images across seven lesion categories. At its core is a specially designed Convolutional Neural Network (CNN) enhanced with residual blocks and attention mechanisms, trained on a Colab Pro A100 GPU. The model outperformed popular pretrained networks—ResNet50, DenseNet121, and EfficientNetB0—achieving a validation accuracy of 85.08, while the best pretrained model reached only 59.63. For practical deployment, the model was converted to TensorFlow Lite and embedded into two cross-platform Flutter Firebase mobile apps: a Patient App for AI-based skin image analysis and appointment booking, and a Doctor App for appointment management and AI-assisted diagnosis. This work delivers an efficient, scalable solution for early skin disease detection and smart healthcare support note de thèses : Mémoire de master en informatiques

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
MF 03-10	MF 03-10	CD	BIBLIOTHEQUE DE FACULTE DES SCIENCES	théses (sci)	Disponible

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