Titre :	Evaluating the impact of region of interest detection methods on medical image classification
Type de document :	document multimédia
Auteurs :	Sarah Derouiche, Auteur ; Asma Merrad, Auteur ; Younes Guellouma, Directeur de thèse
Editeur :	Laghouat : Université Amar Telidji - Département d'informatique
Année de publication :	2025
Importance :	86 p.
Accompagnement :	1 disque optique numérique (CD-ROM)
Note générale :	Option : Data sience and artificial intelligence
Langues :	Anglais
Mots-clés :	Medical image classification  Region of interest (ROI)  Grad-CAM  Deep Learning  Chest X-ray  Brain MRI
Résumé :	Medical image classification remains a challenging task due to the subtle and varied nature of disease patterns across imaging modalities. Deep learning models offer promising solutions; however, the integration of region-of-interest (ROI) detection into the training process is still not well understood.This thesis explores the effectiveness of Grad-CAM as an unsupervised ROI detection method within a two-phase framework.In Phase 1, Grad-CAM is used to generate ROI-focused images from chest X-rays and brain MRIs without requirin pixel level annotations. In Phase 2, we train and compare deep classification models using both these ROI- based inputs and the original full images. The architecture consists of a pretrained convolutional backbone (EfficientNetB4 or DenseNet201), a custom classification head, and two fine-tuning strategies: frozen and partially unfrozen (top 25 % trainable layers). Results show that full-image inputs consistently outperform ROI-transformed versions, with DenseNet201 and partial unfreezing achieving the highest accuracy (98.00 % on chest X-rays, 99.00 % on brain MRIs). These findings indicate that while Grad-CAM is valuable for visual interpretation, it may not serve as an effective unsupervised ROI`detector during training, as it`may exclude contextual cues critical for robust learning.
note de thèses :	Mémoire de master en informatique

Evaluating the impact of region of interest detection methods on medical image classification [document multimédia] / Sarah Derouiche, Auteur ; Asma Merrad, Auteur ; Younes Guellouma, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 86 p. + 1 disque optique numérique (CD-ROM).
Option : Data sience and artificial intelligence
Langues : Anglais

Mots-clés :	Medical image classification  Region of interest (ROI)  Grad-CAM  Deep Learning  Chest X-ray  Brain MRI
Résumé :	Medical image classification remains a challenging task due to the subtle and varied nature of disease patterns across imaging modalities. Deep learning models offer promising solutions; however, the integration of region-of-interest (ROI) detection into the training process is still not well understood.This thesis explores the effectiveness of Grad-CAM as an unsupervised ROI detection method within a two-phase framework.In Phase 1, Grad-CAM is used to generate ROI-focused images from chest X-rays and brain MRIs without requirin pixel level annotations. In Phase 2, we train and compare deep classification models using both these ROI- based inputs and the original full images. The architecture consists of a pretrained convolutional backbone (EfficientNetB4 or DenseNet201), a custom classification head, and two fine-tuning strategies: frozen and partially unfrozen (top 25 % trainable layers). Results show that full-image inputs consistently outperform ROI-transformed versions, with DenseNet201 and partial unfreezing achieving the highest accuracy (98.00 % on chest X-rays, 99.00 % on brain MRIs). These findings indicate that while Grad-CAM is valuable for visual interpretation, it may not serve as an effective unsupervised ROI`detector during training, as it`may exclude contextual cues critical for robust learning.
note de thèses :	Mémoire de master en informatique