Anatomical context improves deep learning on the brain age estimation task
بهبود زمینه آناتومیک یادگیری عمیق بر روی تخمین سن مغز-2019
Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network design to include traditional structural imaging features alongside deep convolutional ones and illustrate this approach on the task of imaging-based age prediction in two separate contexts: T1-weighted brain magnetic resonance imaging (MRI) (N=5121, ages 4–96, healthy controls) and computed tomography (CT) of the head (N=1313, ages 1–97, healthy controls). In brain MRI, we can predict age with a mean absolute error of 4.08 years by combining raw images along with engineered structural features, compared to 5.00 years using image-derived features alone and 8.23 years using structural features alone. In head CT, we can predict age with a median absolute error of 9.99 years combining features, compared to 11.02 years with image-derived features alone and 13.28 years with structural features alone. These results show that we can complement traditional feature estimation using deep learning to improve prediction tasks. As the field of medical image processing continues to integrate deep learning, it will be important to use the new techniques to complement traditional imaging features instead of fully displacing them.
Keywords: Deep learning | Convolutional neural networks | Brain age | Medical image processing
A neuro-heuristic approach for recognition of lung diseases from X-ray images
یک روش عصبی و اکتشافی برای شناخت بیماری های ریه از تصاویر اشعه ایکس-2019
Background and objective: The X-ray screening is one of the most popular methodologies in detection of respiratory system diseases. Chest organs are screened on the film or digital file which go to the doctor for evaluation. However, the analysis of x-ray images requires much experience and time. Clinical decision support is very important for medical examinations. The use of Computational Intelligence can simulate the evaluation and decision processes of a medical expert. We propose a method to provide a decision support for the doctor in order to help to consult each case faster and more precisely. Methods: We use image descriptors based on the spatial distribution of Hue, Saturation and Brightness values in x-ray images, and a neural network co-working with heuristic algorithms (Moth-Flame, Ant Lion) to detect degenerated lung tissues in x-ray image. The neural network evaluates the image and if the possibility of a respiratory disease is detected, the heuristic method identifies the degenerated tissues in the x-ray image in detail based on the use of the proposed fitness function. Results: The average accuracy is 79.06% in pre-detection stage, similarly the sensitivity and the specificity averaged for three pre-classified diseases are 84.22% and 66.7%, respectively. The misclassification errors are 3.23% for false positives and 3.76% for false negatives. Conclusions: The proposed neuro-heuristic approach addresses small changes in the structure of lung tissues, which appear in pneumonia, sarcoidosis or cancer and some consequences that may appear after the treatment. The results show high potential of the newly proposed method. Additionally, the method is flexible and has low computational burden.
Keywords: Medical image processing | Clinical decision support | Neural networks | Heuristic methods
پردازش، تجزیه و تحلیل و تجسم تصویربرداری پزشکی در تحقیقات بالینی
سال انتشار: 2002 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 11
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