3. Producción
Browse
4 results
Search Results
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Twisted tensor products of K3 with K3(Bellwether Publishing, Ltd., 2021-01-01)Jorge A. Guccione and Juan J. Guccione were supported by UBACyT 20020150100153BA (UBA) and PIP 11220110100800CO (CONICET). This work was supported by CONCYTEC-FONDECYT within the framework of the contest ?Proyectos de Investigaci?n B?sica 2020-01? [contract number 120-2020-FONDECYT]. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A feature extraction using probabilistic neural network and BTFSC-Net model with deep learning for brain tumor classification(Multidisciplinary Digital Publishing Institute (MDPI), 2022-12-31)BACKGROUND AND OBJECTIVES: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. MATERIALS AND METHODS: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images' slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. RESULTS: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. CONCLUSIONS: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Learning-Based Computer-Aided Diagnosis Model for the Identification and Classification of Mammography Images(Springer, 2023-09-01)Cancer of the breast is an illness that has the potential to be fatal for females all over the world. Even with the advancements that have been made in treatment, breast cancer cannot be prevented or cured; however, with early identification, one's life expectancy can be increased. A woman's overall health can be improved, which can add years to her life expectancy, if breast cancer is detected at an earlier stage. Radiological screening is a well-known method that is utilised for cancer prevention and detection in significant amounts. Mammograms have the ability to detect breast cancer as well as tumours that may be present in the breast. Recent study has demonstrated that DL-based CAD models can assist radiologists in establishing automated diagnosis of breast cancer in patients. The DL-based CAD model helps radiologists diagnose breast cancer automatically, according to recent research. DL techniques utilising convolutional neural network have gained interest because to their effectiveness in automating data feature representation and maximising accuracy by merging classification and feature representations. It successfully diagnoses clinical pictures. The research aims to build DL-based breast cancer diagnosis models and to review state-of-the-art ML and DL models for breast cancer diagnosis and classification. The research also examines the performance of the proposed models on the benchmark dataset. Sensitivity, specificity, accuracy, and F-measure measure performance. The experimental results showed that the proposed models are effective compared to modern methods. The proposed models are effective for breast cancer diagnosis and categorization. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Learning and Machine Learning Applied to the Detection and Classification of Volcano-Seismic Events at Piton de la Fournaise Volcano(Birkhäuser, 2025-10-01)The Piton de la Fournaise volcano (PdlF) on the island of La Réunion is one of the most active and best monitored volcanoes in the world. Its frequent eruptions make it a natural laboratory for developing new methods and evaluating their performance over multiple eruption sequences. In this work, we present a Deep Learning (DL) model for volcanic earthquake detection and two models for classification based on DL and Machine Learning (ML) algorithms. The detection model is based on encoder–decoder layers that extract high-order features in the time domain that are hidden in the seismograms. The first classification model consists of a simple convolutional neural network that uses the short-time Fourier transform of the signals as input data. A second classifier is based on ML approach and uses hand-crafted features. We show that our detection model, trained on ~ 7 000 volcano-seismic events recorded at PdlF between 2014 and 2021, outperforms previous DL-based models in detecting volcano-seismic events, achieving an accuracy of 98.15% on the testing dataset. Seven classes of signals are considered for classification models: volcano-tectonic (VT) events, rockfall, long-period events, volcanic tremors, tectonic events, anthropogenic noise and environmental noise. Both tested classification models achieve an accuracy of 96.55% in the testing dataset. By applying these models to the continuous data recorded at PdlF in 2019, we are able to detect and classify 1.5 times more VT events than the catalog provided by the Observatory. The detection model takes 28 s to process 24 h seismograms and from a few to a maximum of 70 s for classification.Scopus© Citations 1 3