Transurethral Needle Ablation
(TUNA): Thermal Gradient Mapping
and Comparison of Lesion Size in a
Tissue Model and in Patients with
Benign Prostatic Hyperplasia

Js, Rasor; Ar, Zlotta; Sd, Edwards; Cc, Schulman

doi:10.1159/000474339

Cited by 43 publications

(1 citation statement)

References 4 publications

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“…Thermal cameras, convert infrared radiation emitted from the object into electrical signals, depending on the heat intensity [8]. Infrared thermography, a non-contact and noninvasive method, has been used in various medical studies on thermoregulation [9], breast cancer detection [10], neonatal follow-up [11], urology [12], and vascular diseases [13].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks-Based Approach to Detect Neonatal Respiratory System Anomalies with Limited Thermal Image

Ervural¹,

Ceylan²

2021

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Respiratory system diseases in neonates are thought-about major causes of neonatal morbidity and mortality, particularly in developing countries Early diagnosis and management of these diseases is very important. Thermal imaging stands out as a harmless non-ionizing method, and monitoring of temperature changes or thermal symmetry is used as a diagnostic tool in medicine. This study aims to detect respiratory abnormalities of neonates by artificial intelligence using limited thermal image. Convolutional neural network (CNN) models, although a powerful classification tool, require a balanced and large amount of data. The conditions that require the attention of infants in neonatal intensive care units make medical imaging difficult. It may not always be possible to have much data in the neonatal thermal image database, as in some real-world problems. To overcome this, an effective deep learning model and various data enhancement techniques were used and their effects on the classification results were observed. Neonates with respiratory abnormalities were evaluated in one class, with cardiovascular diseases and abdominal abnormalities were evaluated in the other class. As a result, when the number of images is increased by 4 times with data augmentation, it was determined that the classification accuracy increased from 84.5% to 90.9%.

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