Computed tomography scans image processing for nasal symptoms severity prediction

Abstract

This paper aims to use a new technique of computed tomography (CT) scan image processing to correlate the image analysis with sinonasal symptoms. A retrospective cross-sectional study is conducted by analyzing the digital records of 50 patients who attended the ear, nose and throat (ENT) clinics at King Abdullah University Hospital, Jordan. The coronal plane CT scans are analyzed using our developed software. The purposes of this software are to calculate the surface area of the nasal passage at three different levels visible on coronal plane CT scans: i) the head of the inferior turbinate, ii) the head of the middle turbinate, and iii) the tail of the inferior turbinate. We employ image processing techniques to correlate the narrowing of nasal surface area with sinonasal symptoms. As a consequence, obstruction in the first level is correlated significantly with the symptoms of nasal obstruction while the narrowing in the second level is related to frontal headache. No other significant correlations are found with nasal symptoms at the third level. In our study, we find that image processing techniques can be very useful to predict the severity of common nasal symptoms and they can be used to suggest treatment and to follow up on the case progression.

An Evolutionary Hybrid Convolutional Deep Learning Network to Classify Paddy Leaf Disease

“…If this type of disease is not treated in advance with proper methodology, the quality and output of grain will be drastically decreased [9][10][11][12]. Leaf scald symptoms include reddish-brown broad bands or golden borders at the leaf edges [13][14][15][16].…”

“…MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content. In [16] proposed an analysis of insect attack and nutrient deficiency and disease in coconut leaves. Monitoring of coconut leaves after the application of pesticides and fertilizers has been performed using cutting-edge machine learning and image processing methods.…”