فهرس المقالات CT Scan

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  • صفحة الملخص
  • نص كامل
  
  1 - An Automatic Model Combining Descriptors of Gray-Level Co-Occurrence Matrix and HMAX Model for Adaptive Detection of Liver Disease in CT Images
  Sanaz Bagheri Somayeh Saraf Esmaili
  Liver cancer emerges as a mass in the right upper of the abdomen with general symptoms such as jaundice and ‎weakness. In recent years, the liver cancer has been responsible for increasing the rate of deaths. Due to some discrepancies in the ‎analytical results أکثر

  Liver cancer emerges as a mass in the right upper of the abdomen with general symptoms such as jaundice and ‎weakness. In recent years, the liver cancer has been responsible for increasing the rate of deaths. Due to some discrepancies in the ‎analytical results of CT images and the disagreement among specialists about different parts of the liver, ‎accurate diagnosis of possible conditions requires skill, experience, and precision. In this paper, a new ‎integrative model based on image processing techniques and machine learning is provided, which is used for ‎segmentation of damages caused by the liver disease on CT images. The implementation process consists of three ‎steps: (1) using discrete wavelet transform to remove noise and separate the region of interest (ROI) in the image; (2) ‎creating the recognition pattern based on feature extraction by Gray-Level Co-occurrence matrix and ‎hierarchical visual HMAX model; reducing the feature dimensions is also optimized by principle ‎component analysis and support vector machine (SVM) classification, and finally (3) evaluating the algorithm performance by using K-fold method. The results of implementation were satisfactory both in performance evaluation and use of ‎features selection. The mean recognition accuracy on test images was 91.7%. The implementation was in the ‎presence of both descriptors irrespective of feature dimension ‎reduction; with unique HMAX model and feature ‎dimension reduction and application of both ‎descriptors and reduction of feature dimensions and their effect ‎on recognition were measured.‎ تفاصيل المقالة
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  2 - An Ensemble Deep Learning Model for Detection Covid-19 from CT Scan Images
  Habib Izadkhah
  Diagnosis of covid-19 using deep learning on CT scan images can play an important role in helping doctors. In this paper, by combining EfficientNet-B2 and vision transformers (V iT − 1 − 32) neural networks a new deep transfer learning is proposed. For evaluation, con-f أکثر

  Diagnosis of covid-19 using deep learning on CT scan images can play an important role in helping doctors. In this paper, by combining EfficientNet-B2 and vision transformers (V iT − 1 − 32) neural networks a new deep transfer learning is proposed. For evaluation, con-fusion matrix, precision, accuracy, recall, and F1 score are used. The experimental results are 0.9838 for validation accuracy, 0.9667 for test accuracy, and 0.9839 for accuracy. تفاصيل المقالة