Browsing by Author "Mohammed, Ammar"

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Deep Learning-Based Alzheimer’s Disease Classification: An Experimental Study
(IEEE, 2023-07) Ayman, Mohamed; Darwish, Farah; Mohammed, Tukka; Mohammed, Ammar
Alzheimer's disease poses a significant challenge to healthcare professionals due to its prevalence in dementia cases. Accurate and timely diagnosis is essential for effective management of patients. Magnetic resonance imaging (MRI) has emerged as a vital tool for diagnosing Alzheimer's disease. This paper evaluates the effectiveness of image classification models in detecting Alzheimer's disease using MRI images, with four categories of Alzheimer's disease ranging from no dementia to very mild, mild, and moderate dementia. The study employs and fine-tunes different CNN-based model including VGG16, Inception, and ResNetV2. In order to ensure greater reliability and robustness of the results obtained, we employ cross-validation during the experimentation phase, with different test splits. The experimental results demonstrate that fine-tuning VGG16 yields the highest accuracy of 98.810%. These findings suggest that further optimization and refinement of these models may lead to enhanced accuracy in MRI-based Alzheimer's disease diagnosis, potentially revolutionizing how this condition is managed.
An Enhanced Deep Learning Approach for Breast Cancer Detection in Histopathology Images
(Springer, 2023-03) Ouf, Mahmoud; Abdul-Hamid, Yasser; Mohammed, Ammar
An Enhanced Deep Learning Approach for Breast Cancer Detection in Histopathology Images Mahmoud Ouf, Yasser Abdul-Hamid & Ammar Mohammed Conference paper First Online: 01 March 2023 129 Accesses Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 164) Abstract Breast cancer is defined as abnormal cellular proliferation in the breast. The most common kind of cancer that affects the breast and causes mortality in women is invasive ductal carcinoma (IDC). As a result, early diagnosis and prognosis have become critical to maximize survival and minimize mortality. Mammograms, computerized tomography (CT) scans, and ultrasounds are among the breast cancer tests available. On the other hand, histopathology evaluation with a biopsy is regarded as one of the most trustworthy techniques for determining if suspicious lesions are malignant. This paper proposes an enhanced approach for classifying breast tumors using a proposed CNN model that we named (CancerNet). We evaluate the proposed model on a benchmark dataset containing 277,524 patches. Compared to several types of CNN-based models, our proposed model has achieved accuracy, Area Under Curve (AUC), precision, recall, and F1-score of 86%, 92%, 81%, 84%, and 83%, respectively, outperforming the previous work on the same benchmark.
A Novel Two-Phase Approach for Enhancing Process Model Discovery in Processing Mining
(IEEE, 2023-07) Abo Khedra, M. M; Mohammed, Ammar; Abdel-Hamid, Yasser
Process mining showed great capabilities in many fields, aiming to automatically extract the nature of process models from "event logs."Businesses commonly use process mining to improve key performance indicators (KPIs). Positive records in an event log are used instead of negative ones to achieve KPIs. Fitness, simplicity, accuracy, and generalization are the four primary quality forces for process models, and the current process discovery algorithms commonly take into account only a maximum of two of them. Thus, this paper introduces a novel two-phase approach. Phase one focuses on event log preprocessing by applying K-means clustering to divide the event logs into positive and negative groups according to established key performance indicators. The second phase addresses process discovery by balancing the four quality forces for the process model using the ETM process discovery algorithm. Using three publicly accessible real-life benchmark datasets, we run several experiments and measure the performance of the two-phase approach using the RapidProM workflow tool. The experimental findings reveal that the proposed two-phase approach model gets significant value from the negative records. The ETM process discovery algorithm performs well across the four primary quality forces.