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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Yousuf, Rami
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Computer Science & IT Engineering

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Image classification in cultural heritage Sabha, Muath; Saffarini, Muhammed; Yousuf, Rami
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4722-4735

Abstract

In this paper, an automated supervised image classification technique, specifi- cally for classifying images in the cultural heritage domain, is developed. The developed technique classifies images according to a particular date, culture, people and historical age. The proposed technique consists of two stages, fea- ture extraction using the unsupervised segmentation technique, and the classi- fication stage using supervised classification techniques. Common features are extracted, and their histograms are applied to three classifiers: k-nearest neigh- bor (KNN), logistic regression (LR), and decision tree (DT). When our tech- nique was applied to a repository of images from cultural heritage, it showed reduced complexity and improved classification accuracy. DT has achieved a higher weighted average recall. This is also represented by the weighted av- erage f-measure where DT has obtained 0.81. DT has outperformed the other classifiers in terms of classifying heritage images.
Hybrid model detection and classification of lung cancer Yousuf, Rami; Daraghmi, Eman Yaser
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1496-1506

Abstract

Lung cancer ranks among the most prevalent malignancies worldwide. Early detection is pivotal to improving treatment outcomes for various cancer types. The integration of artificial intelligence (AI) into image processing, coupled with the availability of comprehensive historical lung cancer datasets, provides the chance to create a classification model based on deep learning, thus improving the precision and effectiveness of detecting lung cancer. This not only aids laboratory teams but also contributes to reducing the time to diagnosis and associated costs. Consequently, early detection serves to conserve resources and, more significantly, human lives. This study proposes convolutional neural network (CNN) models and transfer learning-based architectures, including ResNet50, VGG19, DenseNet169, and InceptionV3, for lung cancer classification. An ensemble approach is used to enhance overall cancer detection performance. The proposed ensemble model, composed of five effective models, achieves an F1-score of 97.77% and an accuracy rate of 97.5% on the IQ-OTH/NCCD test dataset. These findings highlight the effectiveness and dependability of our novel model in automating the classification of lung cancer, outperforming prior research efforts, streamlining diagnosis processes, and ultimately contributing to the preservation of patients' lives.
Co-Authors Daraghmi, Eman Yaser Sabha, Muath Saffarini, Muhammed