@proceedings {6447,
	title = {Characterization of Melanoma Using Convolutional Neural Networks and Dermoscopic Images},
	journal = {International Conference on Electrical Engineering and Control Applications},
	year = {2019},
	pages = {1147-1155},
	publisher = {Springer},
	author = {Abdelghani Tafsast and Mohamed Laid Hadjili and Ayache Bouakaz and Nabil Benoudjit}
}
@proceedings {6445,
	title = {Automatic microemboli characterization using convolutional neural networks and radio frequency signals},
	journal = {2018 International Conference on Communications and Electrical Engineering (ICCEE)},
	year = {2018},
	pages = {1-4},
	publisher = {IEEE},
	isbn = {1728101123},
	author = {Abdelghani Tafsast and Karim Ferroudji and Mohamed Laid Hadjili and Ayache Bouakaz and Nabil Benoudjit}
}
@article {6442,
	title = {Unsupervised cluster-based method for segmenting biological tumour volume of laryngeal tumours in 18 F-FDG-PET images},
	journal = {IET Image Processing},
	volume = {11},
	number = {6},
	year = {2017},
	pages = {389-396},
	publisher = {IET},
	isbn = {1751-9659},
	author = {Abdelghani Tafsast and Mohamed Laid Hadjili and Ayache Bouakaz and Nabil Benoudjit}
}
@article {3145,
	title = {Unsupervised cluster-based method for segmenting biological tumor volume of laryngeal tumors in 18F-FDG-PET images},
	journal = {IET Image Processing},
	volume = {11},
	number = {6},
	year = {2016},
	pages = {389-396},
	abstract = {
	In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in\&nbsp;18F-FDG-PET images. The technique is based on the k-means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7\%) compared with other existing methods in clinical routine, including fuzzy c-means with (35.58\%), gradient-based method with (19.14\%) and threshold-based methods.

},
	author = {Abdelghani Tafsat and Mohamed Laid Hadjili and Ayache Bouakaz and Nabil Benoudjit}
}
