EBSAR: Detecting of Objects that Hinder Visually Impaired in a Controlled Area Using Deep Learning

Aljarf, Ahad; Almaghrabi, Ghaidaa; Albarakati, Halah; Ahmed, Haneen; Alharbi, Raghad Mohammad; Aljuhani, Sawsan

doi:10.1109/nccc57165.2022.10067421

Cited by 2 publications

References 14 publications

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Computer Aided Cervical Cancer Diagnosis Using Gazelle Optimization Algorithm With Deep Learning Model

Nour,

Issaoui,

Edris

et al. 2024

IEEE Access

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Cervical cancer (CC), the most common cancer among women, is most commonly diagnosed through Pap smears, a crucial screening process that includes collecting cervical cells for examination. Artificial intelligence (AI)-powered computer-aided diagnoses (CAD) system becomes a promising tool for improving CC diagnosis. Deep learning (DL), a branch of AI, holds particular potential in CAD systems for early detection and accurate diagnosis. DL algorithm is trained to identify abnormalities and patterns in Pap smear images, such as dysplasia, cellular changes, and other markers of CC. So, this study presents a Computer Aided Cervical Cancer Diagnosis utilizing the Gazelle Optimizer Algorithm with Deep Learning (CACCD-GOADL) model on Pap smear images. The foremost objective of the CACCD-GOADL approach is to examine the image detection of CC. To accomplish this, the CACCD-GOADL methodology uses an improved MobileNetv3 model for extracting complex patterns in Pap smear images. In addition, the CACCD-GOADL technique designs a new GOA for the hyperparameter tuning of the improved MobileNetv3 system. For the classification and identification of cancer, the CACCD-GOADL technique uses a stacked extreme learning machine (SELM) methodology. The simulation validation of the CACCD-GOADL approach is verified on a benchmark dataset of Herlev. Experimental results highlighted that the CACCD-GOADL algorithm reaches superior outcomes over other methods.

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