Nasser Edinne BENHASSINE - Academia.edu (original) (raw)

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Papers by Nasser Edinne BENHASSINE

International journal of informatics and applied mathematics, Jan 17, 2024

Breast cancer can progress silently in its early stages and frequently without noticeable symptom... more Breast cancer can progress silently in its early stages and frequently without noticeable symptoms. However, it poses a serious risk to women. It is imperative to recognize this potential health concern to mitigate it early. In the last few years, Convolutional Neural Networks (CNNs) have advanced significantly in their ability to classify images of breast cancer. Their capacity to automatically extract discriminant features from images has enhanced the performances and accuracy of image classification tasks. They outperform state-of-the-art techniques in this area. Furthermore, complicated models that were first learned for certain tasks can be easily adapted to complete new tasks by using transfer-learning approaches. However, deep learning-based categorization techniques could experience overfitting issues, particularly in cases where the dataset is small. The primary goal of this work is to investigate the performances of certain deep learning models to classify breast cancer images and to study the effects of data augmentation techniques, such as image rotation or displacement when utilizing a transfer learning approach. Using certain image datasets, the ResNet18, Resnet50, and VGG16 models demonstrated accuracy improvements, according to our experimental results.

Biomedical Engineering / Biomedizinische Technik

Although fetal phonocardiogram (fPCG) signals have become a good indicator for discovered heart d... more Although fetal phonocardiogram (fPCG) signals have become a good indicator for discovered heart disease, they may be contaminated by various noises that reduce the signals quality and the final diagnosis decision. Moreover, the noise may cause the risk of the data to misunderstand the heart signal and to misinterpret it. The main objective of this paper is to effectively remove noise from the fPCG signal to make it clinically feasible. So, we proposed a novel noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), wavelet threshold and Crow Search Algorithm (CSA). This noise reduction method, named ICEEMDAN-DWT-CSA, has three major advantages. They were, (i) A better suppress of mode mixing and a minimized number of IMFs, (ii) A choice of wavelet corresponding to the study signal proven by the literature and (iii) Selection of the optimal threshold value. Firstly, the noisy fPCG signal is decomposed into Intrinsic Mode ...

Real Perspective of Fourier Transforms and Current Developments in Superconductivity, 2021

Medical imaging systems are very important in medicine domain. They assist specialists to make th... more Medical imaging systems are very important in medicine domain. They assist specialists to make the final decision about the patient’s condition, and strongly help in early cancer detection. The classification of mammogram images represents a very important operation to identify whether the breast cancer is benign or malignant. In this chapter, we propose a new computer aided diagnostic (CAD) system, which is composed of three steps. In the first step, the input image is pre-processed to remove the noise and artifacts and also to separate the breast profile from the pectoral muscle. This operation is a difficult task that can affect the final decision. For this reason, a hybrid segmentation method using the seeded region growing (SRG) algorithm applied on a localized triangular region has been proposed. In the second step, we have proposed a features extraction method based on the discrete cosine transform (DCT), where the processed images of the breast profiles are transformed by th...

Journal of Mechanics in Medicine and Biology, 2020

The Computer-Aided Diagnostic (CAD) system is an important tool that helps radiologists to provid... more The Computer-Aided Diagnostic (CAD) system is an important tool that helps radiologists to provide a second opinion for the early detection of breast cancer and therefore, aids to reduce the mortality rates. In this work, we try to develop a new (CAD) system to classify mammograms into benign or malignant. The proposed system consists of three main steps. The preprocessing stage consists of noise filtering, elimination of unwanted objects and suppressing the pectoral muscle. The Seeded Region Growing (SRG) segmentation technique is applied in a triangular region that contains the pectoral muscle to localize it and extract the region of interest (ROI). The features extraction step is performed by applying the discrete wavelet transform (DWT) to each obtained ROI, and the most discriminating coefficients are selected using the discrimination power analysis (DPA) method. Finally, the classification is carried out by the support vector machine (SVM), artificial neural networks (ANN), ra...

Biomedical Engineering / Biomedizinische Technik, 2019

The greatest problem with recording heart sounds is parasitic noise effects. A reasonable solutio... more The greatest problem with recording heart sounds is parasitic noise effects. A reasonable solution to reduce noise can be carried out by minimization of extraneous noises in the vicinity of the patient during recording, in addition to the methods of signal processing that must be effective in noisy environments. Wavelet transform has become an essential tool for many applications, but its effectiveness is influenced by main parameters. Determination of mother wavelet function and decomposition level (DL) are important key factors to demonstrate the advantages of wavelet denoising. So, selection of optimal mother wavelet with DL is a main challenge to current algorithms. The principal aim of this study was the choice of an appropriate criterion for finding the optimal DL and the optimal mother wavelet function according to four criteria which are: signal-to-noise ratio (SNR), mean square error (MSE), percentage root-mean-square difference (PRD) and the structure similarity index meas...

International Journal of Imaging Systems and Technology, 2019

The purpose of this work is to develop a computer-aided diagnosis (CAD) system to assist radiolog... more The purpose of this work is to develop a computer-aided diagnosis (CAD) system to assist radiologists in the classification of mammogram images. The CAD system is composed of three main steps. The first step is image preprocessing and segmentation with the seeded region growing algorithm applied on a localized triangular region to remove only the muscle. In the second step of the CAD system, we proposed a novel features extraction method, which consists of three stages. In the first, the discrete cosine transform (DCT) is applied on all obtained regions of interest and then only the upper left corner (ULC) of DCT coefficients is retained. Second, we have applied the energy probability to the ULCs that is used as a criterion for selecting discriminant information. At the last stage, a new Most Discriminative power coefficient algorithm has been proposed to select the most significant features. In the final step of the CAD, the support vector machines, Naive Bayes, and artificial neural network (ANN) classifiers are used to make an effective classification. The evaluation of the proposed algorithm on the mini-Mammographic Image Analysis Society database shows its efficiency over other recently proposed CAD systems in the literature, whereas an accuracy of 100% can be achieved using ANN with a small number of features.