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Papers by wiem abbes

Research paper thumbnail of Ontology based approach using a systemic knowledge model for surface defect classification

Fifteenth International Conference on Machine Vision (ICMV 2022)

Research paper thumbnail of Fuzzy Ontology for Automatic Skin Lesion Classification

Journal of Testing and Evaluation, 2021

Medical diagnosis of cancer is becoming more complex in recent years, with doctors focusing on ma... more Medical diagnosis of cancer is becoming more complex in recent years, with doctors focusing on making both earlier and accurate diagnoses to save patients’ lives. Such goals are more challenging for melanoma, which is the deadliest of skin cancers. Recently, melanoma incidence has increased significantly because of climate change. Fortunately, early detection leads to a 5-year survival rate of 98 %. Computer-aided diagnosis systems can offer a more objective analysis tool, taking into consideration the expert’s knowledge. Ontology offers an efficient framework for reducing the gap between low-level information and expert analysis. A dermatologist’s recommendation is often based on the ABCD rule, involving four characteristics of a lesion, which are asymmetry, border, color, and differential structures. A score, associated to a qualitative description of the lesion, allows lesions to be categorized into three classes: melanoma, benign, or recommended follow-up. Early research on auto...

Research paper thumbnail of Deep Neural Networks for Melanoma Detection from Optical Standard Images using Transfer Learning

Procedia Computer Science, 2021

Research paper thumbnail of Fuzzy decision ontology for melanoma diagnosis using KNN classifier

Multimedia Tools and Applications, 2021

Melanoma is the most dangerous type of skin cancer when discovered in an advanced stage. Early de... more Melanoma is the most dangerous type of skin cancer when discovered in an advanced stage. Early detection of melanoma improves survival. Several Computer -Aided Diagnosis (CAD) systems are currently developed to speed up early diagnosis. Recently, ontology is widely adapted for describing and diagnosing a disease. For melanoma detection, the ontology reasoning of dermatologists is based on expert rules, such as ABCD rule. Accordingly, dermatologists classify skin lesions in three classes: melanoma, benign, and recommended follow-up class. In this paper, we propose a CAD system based on an ontology for melanoma diagnosis by giving the probability of being melanoma. We first present our ontology focusing on its main concepts involved in ABCD rule: Asymmetry, Border, Color and Differential structures. Accordingly, the Bag-of-Words, modeling these concepts, are generated from extracted features of skin lesion images. An important step in ontology is to define rules relating the different concepts. In our case, these rules allow the fuzzy decision to classify lesion in melanoma, benign or recommended follow-up class with a malignancy probability. Considering the similarity of melanoma cases, the K-Nearest Neighbors approach is applied to make the final decision in case of a recommended follow-up class. Experimental validation on two public datasets of 206 lesion images shows that our approach presents an efficient method of analysis and can be more appropriate for lesion severity classification. It yields a sensitivity of (96%) and an accuracy of (92%), surpassing existing recent approaches on melanoma diagnosis.

Research paper thumbnail of Automatic Skin Lesions Classification Using Ontology-Based Semantic Analysis of Optical Standard Images

Procedia Computer Science, 2017

This paper describes ontology-based semantic analysis of lesion images. We first present our onto... more This paper describes ontology-based semantic analysis of lesion images. We first present our ontology focusing on its main concepts, as well as the semantic annotation. Accordingly, the Bag-of-Words (BoW), modeling these concepts in skin lesion diagnosis, is inspired from experts in dermatology. These BoWs are modeled from the lesion images. Firstly, we extract low-level features describing the lesion shape, color and texture. Secondly, the BoWs are generated from these features using a machine learning classifier (SVM). An important step in semantic analysis is to define rules relating the different concepts. In our case, these rules are inspired from the score of the ABCD rule for decision making. Experimental results on a public database of 206 lesion images demonstrate that ontology offers a more efficient frame of analysis, where semantic relations between concepts can handle more knowledge of experts, and can be more appropriate for lesion severity classification with a good accuracy. Comparing to the previous works, our approach yields good sensitivity (97.4%) and accuracy (76.9%).

Research paper thumbnail of High-level features for automatic skin lesions neural network based classification

2016 International Image Processing, Applications and Systems (IPAS), 2016

Melanoma is the most dangerous form of skin cancer. It can be developed from pigmented cells of t... more Melanoma is the most dangerous form of skin cancer. It can be developed from pigmented cells of the skin and can grow and spread swiftly to other organs (metastasis). An early diagnosis increases the chance of cure. In the past three decades, the increase in the incidence of melanoma has given rise to more accurate methods of analysis. Feature extraction is a critical step in melanoma decision support systems. Early dermatoscopic rules (ABCD rule, 7-point checklist, Menzies method and CASH algorithm), used by experts are generally low level features. In this paper, we consider several dermatoscopic rules for automatic detection of melanoma in order to generate new high level features allowing semantic analysis. Such extracted features are based on shape characterization and color and texture features. A neural network classifier is used for decision making. Experimental results indicate that semantic analysis is a useful method for discrimination of melanocytic skin tumors with good accuracy. The proposed method yields a good sensitivity of 92% and a specificity of 95% on a database of 206 skin lesion images. A comparative study with recent previous works illustrates that our approach outperforms in terms of accuracy and specificity.

Research paper thumbnail of Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2019

Melanoma is the most serious type of skin cancer. We consider in this paper diagnosing melanoma b... more Melanoma is the most serious type of skin cancer. We consider in this paper diagnosing melanoma based on skin lesion images obtained by common optical cameras. Given the lower quality of such images, we should cope with the imprecision of image data. This paper proposes a CAD system for decision making about the skin lesion severity. We first define the fuzzy modeling of the Bag-of-Words (BoW) of the lesion. Indeed, features are extracted from the skin lesion image related to four criteria inspired by the ABCD rule (Asymmetry, Border, Color, and Differential structures). Based on Fuzzy C-Means (FCM), membership degrees are determined for each BoW. Then, a deep neural network classifier is used for decision making. Based on a public database of 206 lesion images, experimental results demonstrate that the fuzzification of feature modeling presents good results in term of sensitivity (90.1%) and of accuracy (87.5%). A comparative study illustrates that our approach offers the best accuracy and sensitivity.

Research paper thumbnail of Ontology based approach using a systemic knowledge model for surface defect classification

Fifteenth International Conference on Machine Vision (ICMV 2022)

Research paper thumbnail of Fuzzy Ontology for Automatic Skin Lesion Classification

Journal of Testing and Evaluation, 2021

Medical diagnosis of cancer is becoming more complex in recent years, with doctors focusing on ma... more Medical diagnosis of cancer is becoming more complex in recent years, with doctors focusing on making both earlier and accurate diagnoses to save patients’ lives. Such goals are more challenging for melanoma, which is the deadliest of skin cancers. Recently, melanoma incidence has increased significantly because of climate change. Fortunately, early detection leads to a 5-year survival rate of 98 %. Computer-aided diagnosis systems can offer a more objective analysis tool, taking into consideration the expert’s knowledge. Ontology offers an efficient framework for reducing the gap between low-level information and expert analysis. A dermatologist’s recommendation is often based on the ABCD rule, involving four characteristics of a lesion, which are asymmetry, border, color, and differential structures. A score, associated to a qualitative description of the lesion, allows lesions to be categorized into three classes: melanoma, benign, or recommended follow-up. Early research on auto...

Research paper thumbnail of Deep Neural Networks for Melanoma Detection from Optical Standard Images using Transfer Learning

Procedia Computer Science, 2021

Research paper thumbnail of Fuzzy decision ontology for melanoma diagnosis using KNN classifier

Multimedia Tools and Applications, 2021

Melanoma is the most dangerous type of skin cancer when discovered in an advanced stage. Early de... more Melanoma is the most dangerous type of skin cancer when discovered in an advanced stage. Early detection of melanoma improves survival. Several Computer -Aided Diagnosis (CAD) systems are currently developed to speed up early diagnosis. Recently, ontology is widely adapted for describing and diagnosing a disease. For melanoma detection, the ontology reasoning of dermatologists is based on expert rules, such as ABCD rule. Accordingly, dermatologists classify skin lesions in three classes: melanoma, benign, and recommended follow-up class. In this paper, we propose a CAD system based on an ontology for melanoma diagnosis by giving the probability of being melanoma. We first present our ontology focusing on its main concepts involved in ABCD rule: Asymmetry, Border, Color and Differential structures. Accordingly, the Bag-of-Words, modeling these concepts, are generated from extracted features of skin lesion images. An important step in ontology is to define rules relating the different concepts. In our case, these rules allow the fuzzy decision to classify lesion in melanoma, benign or recommended follow-up class with a malignancy probability. Considering the similarity of melanoma cases, the K-Nearest Neighbors approach is applied to make the final decision in case of a recommended follow-up class. Experimental validation on two public datasets of 206 lesion images shows that our approach presents an efficient method of analysis and can be more appropriate for lesion severity classification. It yields a sensitivity of (96%) and an accuracy of (92%), surpassing existing recent approaches on melanoma diagnosis.

Research paper thumbnail of Automatic Skin Lesions Classification Using Ontology-Based Semantic Analysis of Optical Standard Images

Procedia Computer Science, 2017

This paper describes ontology-based semantic analysis of lesion images. We first present our onto... more This paper describes ontology-based semantic analysis of lesion images. We first present our ontology focusing on its main concepts, as well as the semantic annotation. Accordingly, the Bag-of-Words (BoW), modeling these concepts in skin lesion diagnosis, is inspired from experts in dermatology. These BoWs are modeled from the lesion images. Firstly, we extract low-level features describing the lesion shape, color and texture. Secondly, the BoWs are generated from these features using a machine learning classifier (SVM). An important step in semantic analysis is to define rules relating the different concepts. In our case, these rules are inspired from the score of the ABCD rule for decision making. Experimental results on a public database of 206 lesion images demonstrate that ontology offers a more efficient frame of analysis, where semantic relations between concepts can handle more knowledge of experts, and can be more appropriate for lesion severity classification with a good accuracy. Comparing to the previous works, our approach yields good sensitivity (97.4%) and accuracy (76.9%).

Research paper thumbnail of High-level features for automatic skin lesions neural network based classification

2016 International Image Processing, Applications and Systems (IPAS), 2016

Melanoma is the most dangerous form of skin cancer. It can be developed from pigmented cells of t... more Melanoma is the most dangerous form of skin cancer. It can be developed from pigmented cells of the skin and can grow and spread swiftly to other organs (metastasis). An early diagnosis increases the chance of cure. In the past three decades, the increase in the incidence of melanoma has given rise to more accurate methods of analysis. Feature extraction is a critical step in melanoma decision support systems. Early dermatoscopic rules (ABCD rule, 7-point checklist, Menzies method and CASH algorithm), used by experts are generally low level features. In this paper, we consider several dermatoscopic rules for automatic detection of melanoma in order to generate new high level features allowing semantic analysis. Such extracted features are based on shape characterization and color and texture features. A neural network classifier is used for decision making. Experimental results indicate that semantic analysis is a useful method for discrimination of melanocytic skin tumors with good accuracy. The proposed method yields a good sensitivity of 92% and a specificity of 95% on a database of 206 skin lesion images. A comparative study with recent previous works illustrates that our approach outperforms in terms of accuracy and specificity.

Research paper thumbnail of Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2019

Melanoma is the most serious type of skin cancer. We consider in this paper diagnosing melanoma b... more Melanoma is the most serious type of skin cancer. We consider in this paper diagnosing melanoma based on skin lesion images obtained by common optical cameras. Given the lower quality of such images, we should cope with the imprecision of image data. This paper proposes a CAD system for decision making about the skin lesion severity. We first define the fuzzy modeling of the Bag-of-Words (BoW) of the lesion. Indeed, features are extracted from the skin lesion image related to four criteria inspired by the ABCD rule (Asymmetry, Border, Color, and Differential structures). Based on Fuzzy C-Means (FCM), membership degrees are determined for each BoW. Then, a deep neural network classifier is used for decision making. Based on a public database of 206 lesion images, experimental results demonstrate that the fuzzification of feature modeling presents good results in term of sensitivity (90.1%) and of accuracy (87.5%). A comparative study illustrates that our approach offers the best accuracy and sensitivity.