Browsing by Subject "Computer vision"

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    Comparison of 2D and 3D convolutional neural networks in hyperspectral image analysis of fruits applied to orange bruise detection
    (Wiley, 2023-10-25) Pourdarbani, Raziyeh; Sabzi, Sajad; Zohrabi, Reihaneh; García Mateos, Ginés; Fernández Beltrán, Rubén; Molina Martínez, José Miguel; Rohban, Mohammad H.; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
    Recent advances in hyperspectral imaging (HSI) have demonstrated its ability to detect defects in fruit that may not be visible in RGB images. HSIs can be considered 3D images containing two spatial dimensions and one spectral dimension. Therefore, the first question that arises is how to process this type of information, either using 2D or 3D models. In this study, HSI in the 550–900 nm spectral range was used to detect bruising in oranges. Sixty samples of Thompson oranges were subjected to a mechanical bruising process, and HSIs were taken at different time intervals: before bruising, and 8 and 16 h after bruising. The samples were then classified using two convolutional neural network (CNN) models, a shallow 7-layer network (CNN-7) and a deep 18-layer network (CNN-18). In addition, two different input processing approaches are used: using 2D information from each band, and using the full 3D data from each HSI. The 3D models were the most accurate, with 94% correct classification for 3D-CNN-18, compared to 90% for 3D-CNN-7, and less than 83% for the 2D models. Our study suggests that 3D HSI may be a more effective technique for detecting fruit bruising, allowing the development of a fast, accurate, and nondestructive method for fruit sorting.
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    Federated vs local vs central deep learning of tooth segmentation on panoramic radiographs
    (Elsevier, 2023-05-18) Schneider, Lisa; Rischke, Roman; Krois, Joachim; Krasowki, Aleksander; Büttmer, Martha; Mohammad-Rahimi, Hossein; Chaurasai, Akhilanand; Pereira, Nielsen, S.; Lee, Jae-hong; Uribe, Sergio E.; Shahab, Shahriar; Birke Koca-Ünsal, Revan; Ünsal, Gürkan; Martínez Beneyto, Yolanda; Brinz, Janet; Tryfonos, Olga; Schwendicke, Falk; Dermatología, Estomatología, Radiología y Medicina Física
    Objective: Federated Learning (FL) enables collaborative training of artificial intelligence (AI) models from multiple data sources without directly sharing data. Due to the large amount of sensitive data in dentistry, FL may be particularly relevant for oral and dental research and applications. This study, for the first time, employed FL for a dental task, automated tooth segmentation on panoramic radiographs. Methods: We employed a dataset of 4,177 panoramic radiographs collected from nine different centers (n = 143 to n = 1881 per center) across the globe and used FL to train a machine learning model for tooth segmentation. FL performance was compared against Local Learning (LL), i.e., training models on isolated data from each center (assuming data sharing not to be an option). Further, the performance gap to Central Learning (CL), i.e., training on centrally pooled data (based on data sharing agreements) was quantified. Generalizability of models was evaluated on a pooled test dataset from all centers. Results: For 8 out of 9 centers, FL outperformed LL with statistical significance (p<0.05); only the center providing the largest amount of data FL did not have such an advantage. For generalizability, FL outperformed LL across all centers. CL surpassed both FL and LL for performance and generalizability. Conclusion: If data pooling (for CL) is not feasible, FL is shown to be a useful alternative to train performant and, more importantly, generalizable deep learning models in dentistry, where data protection barriers are high. Clinical Significance: This study proves the validity and utility of FL in the field of dentistry, which encourages researchers to adopt this method to improve the generalizability of dental AI models and ease their transition to the clinical environment.