University Sétif 1 FERHAT ABBAS Faculty of Sciences
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Auteur Nour Deghoul |
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Attention-Based Deep Convolutional Neural Network Versus Transfer Learning for Medical Image Classification and Disease Diagnosis / Maroua Azouz
Titre : Attention-Based Deep Convolutional Neural Network Versus Transfer Learning for Medical Image Classification and Disease Diagnosis Type de document : texte imprimé Auteurs : Maroua Azouz, Auteur ; Nour Deghoul, Auteur ; Abdelouahab Moussaoui, Directeur de thèse Année de publication : 2022 Importance : 1 vol (94 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Transfer Learning
Image Classification
ViTIndex. décimale : 004 Informatique Résumé :
Cancer is a disease in which some of the body’s cells grow uncontrollably and spread
to other parts of the body. recently the most two common type of diseases in our city
Setif are Skin cancer and nodules in thyroid gland cancer. Skin Cancer Is the Cancer
You Can See, this sentence is the the motto of Skin Cancer Foundation .When strange
grains appear in the patient’s skin, he cannot differentiate whether it is cancer or just
simple skin ulcers, so in most cases it is not detected except in advanced stages of the
disease . Most patients with nodules in the thyroid gland are afraid to turning these
nodes into cancerous masses, and with the spread of this disease in recent years, this
question has become a concern for all patients.
Recently, the world has become highly focused on deep learning and classification
of medical images by building stable models in computer aided diagnosis [8], most often
the model is using Convolutional neural networks. Now we propose a model based
on the attention mechanism that based on the most important features in the image.
The attention mechanism contributes to increasing the effectiveness of the model and
achieving a better classification. The aim of this study is to improve the accuracy of a
computer-aided diagnosis approach that medical professionals can easily use as an aid.
In this thesis, we proposed the use of the transformer and transfer-learning mechanism
to detect and classify skin cancer diseases and the type of thyroid nodules(benign or malignant).
We collected features from three types of pre-trained models, EfficientNetB7,
VGGNet16, Xception as feature extraction roles. Then as using the features as input to
Vision Transformer (ViT) and then using neural networks for classification.As a result,
the proposed approach achieved accuracy of 83.74% for skin dataset and 76.18% for
thyroid dataset, and an other similar model using CNN model proposed by us is achieve
92.95% of accuracy for skin dataset and 88.18% of accuracy for thyroid dataset . We
proposed, also the of use transfer learning for develop the pre trained model and applied
the same datasets on ResNet50 and our new ResNet50,these models achieve 67.66%,
98.89% respectively for skin cancer data set and they achieve 67.03%, 90.97% respectively
by the use of thyroid dataset.Côte titre : MAI/0587 En ligne : https://drive.google.com/file/d/1x9lI7fG4IlSYzf0xGs-E7AL9ynLyGu1h/view?usp=share [...] Format de la ressource électronique : Attention-Based Deep Convolutional Neural Network Versus Transfer Learning for Medical Image Classification and Disease Diagnosis [texte imprimé] / Maroua Azouz, Auteur ; Nour Deghoul, Auteur ; Abdelouahab Moussaoui, Directeur de thèse . - 2022 . - 1 vol (94 f .) ; 29cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Transfer Learning
Image Classification
ViTIndex. décimale : 004 Informatique Résumé :
Cancer is a disease in which some of the body’s cells grow uncontrollably and spread
to other parts of the body. recently the most two common type of diseases in our city
Setif are Skin cancer and nodules in thyroid gland cancer. Skin Cancer Is the Cancer
You Can See, this sentence is the the motto of Skin Cancer Foundation .When strange
grains appear in the patient’s skin, he cannot differentiate whether it is cancer or just
simple skin ulcers, so in most cases it is not detected except in advanced stages of the
disease . Most patients with nodules in the thyroid gland are afraid to turning these
nodes into cancerous masses, and with the spread of this disease in recent years, this
question has become a concern for all patients.
Recently, the world has become highly focused on deep learning and classification
of medical images by building stable models in computer aided diagnosis [8], most often
the model is using Convolutional neural networks. Now we propose a model based
on the attention mechanism that based on the most important features in the image.
The attention mechanism contributes to increasing the effectiveness of the model and
achieving a better classification. The aim of this study is to improve the accuracy of a
computer-aided diagnosis approach that medical professionals can easily use as an aid.
In this thesis, we proposed the use of the transformer and transfer-learning mechanism
to detect and classify skin cancer diseases and the type of thyroid nodules(benign or malignant).
We collected features from three types of pre-trained models, EfficientNetB7,
VGGNet16, Xception as feature extraction roles. Then as using the features as input to
Vision Transformer (ViT) and then using neural networks for classification.As a result,
the proposed approach achieved accuracy of 83.74% for skin dataset and 76.18% for
thyroid dataset, and an other similar model using CNN model proposed by us is achieve
92.95% of accuracy for skin dataset and 88.18% of accuracy for thyroid dataset . We
proposed, also the of use transfer learning for develop the pre trained model and applied
the same datasets on ResNet50 and our new ResNet50,these models achieve 67.66%,
98.89% respectively for skin cancer data set and they achieve 67.03%, 90.97% respectively
by the use of thyroid dataset.Côte titre : MAI/0587 En ligne : https://drive.google.com/file/d/1x9lI7fG4IlSYzf0xGs-E7AL9ynLyGu1h/view?usp=share [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0587 MAI/0587 Mémoire Bibliothéque des sciences Anglais Disponible
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