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  • 1.
    Qadri, Syed Furqan
    et al.
    Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China; AI Research Center for Medical Image Analysis and Diagnosis, College of Computer Science and Software Engineering, Shenzhen University, Guangdong, Shenzhen, China.
    Lin, Hongxiang
    Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
    Shen, Linlin
    AI Research Center for Medical Image Analysis and Diagnosis, College of Computer Science and Software Engineering, Shenzhen University, Guangdong, Shenzhen, China.
    Ahmad, Mubashir
    Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Tobe Camp, Abbottabad, Pakistan.
    Qadri, Salman
    Computer Science Department, MNS-University of Agriculture, Multan, Pakistan.
    Khan, Salabat
    AI Research Center for Medical Image Analysis and Diagnosis, College of Computer Science and Software Engineering, Shenzhen University, Guangdong, Shenzhen, China.
    Khan, Maqbool
    Software Competence Center Hagenberg GmbH, Softwarepark, Hagenberg, Linz, Austria; Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology, Mang, Haripur, Pakistan.
    Zareen, Syeda Shamaila
    Faculty of Information Technology, Beijing University of Technology, Beijing, China.
    Akbar, Muhammad Azeem
    Lappeenranta University of Technology, Department of Information Technology, Lappeenranta, Finland.
    Bin Heyat, Md Belal
    IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Guangdong, Shenzhen, China; Centre for VLSI and Embedded System Technologies, International Institute of Information Technology, Telangana, Hyderabad, India; Department of Science and Engineering, Novel Global Community Educational Foundation, NSW, Hebersham, Australia.
    Qamar, Saqib
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    CT-based automatic spine segmentation using patch-based deep learning2023Ingår i: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 2023, artikel-id 2345835Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    CT vertebral segmentation plays an essential role in various clinical applications, such as computer-assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is challenging due to the overlapping shadows of thoracoabdominal structures such as the lungs, bony structures such as the ribs, and other issues such as ambiguous object borders, complicated spine architecture, patient variability, and fluctuations in image contrast. Deep learning is an emerging technique for disease diagnosis in the medical field. This study proposes a patch-based deep learning approach to extract the discriminative features from unlabeled data using a stacked sparse autoencoder (SSAE). 2D slices from a CT volume are divided into overlapping patches fed into the model for training. A random under sampling (RUS)-module is applied to balance the training data by selecting a subset of the majority class. SSAE uses pixel intensities alone to learn high-level features to recognize distinctive features from image patches. Each image is subjected to a sliding window operation to express image patches using autoencoder high-level features, which are then fed into a sigmoid layer to classify whether each patch is a vertebra or not. We validate our approach on three diverse publicly available datasets: VerSe, CSI-Seg, and the Lumbar CT dataset. Our proposed method outperformed other models after configuration optimization by achieving 89.9% in precision, 90.2% in recall, 98.9% in accuracy, 90.4% in F-score, 82.6% in intersection over union (IoU), and 90.2% in Dice coefficient (DC). The results of this study demonstrate that our model's performance consistency using a variety of validation strategies is flexible, fast, and generalizable, making it suited for clinical application.

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  • 2.
    Qamar, Saqib
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Öberg, Rasmus
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Malyshev, Dmitry
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Andersson, Magnus
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR).
    A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images2023Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 13, nr 1, artikel-id 18758Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present a new approach to segment and classify bacterial spore layers from Transmission Electron Microscopy (TEM) images using a hybrid Convolutional Neural Network (CNN) and Random Forest (RF) classifier algorithm. This approach utilizes deep learning, with the CNN extracting features from images, and the RF classifier using those features for classification. The proposed model achieved 73% accuracy, 64% precision, 46% sensitivity, and 47% F1-score with test data. Compared to other classifiers such as AdaBoost, XGBoost, and SVM, our proposed model demonstrates greater robustness and higher generalization ability for non-linear segmentation. Our model is also able to identify spores with a damaged core as verified using TEMs of chemically exposed spores. Therefore, the proposed method will be valuable for identifying and characterizing spore features in TEM images, reducing labor-intensive work as well as human bias.

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