Evaluating the impact of region of interest detection methods on medical image classification / Sarah Derouiche

Public ISBD 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 (eng) 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 (eng) 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

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
MF 03-05	MF 03-05	CD	BIBLIOTHEQUE DE FACULTE DES SCIENCES	théses (sci)	Disponible

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