Catalogue des ouvrages Université de Laghouat
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| 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 | | Autre Editeur : | Laghouat : Université Amar Telidji - Département d'informatique | | Importance : | 44 p. | | Accompagnement : | 1 disque optique numérique (CD-ROM) | | Note générale : | Option: Data science et artificial intelligence | | Langues : | Anglais | | 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 : Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 44 p. + 1 disque optique numérique (CD-ROM). Option: Data science et artificial intelligence Langues : Anglais | 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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