Machine-learning-based classification of the histological subtype of non-small-cell lung cancer using MRI texture analysis

Self Cite

Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.

Section: Introductionmentioning

confidence: 99%

Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

Domino

Borowska

Kozłowska

et al. 2021

Self Cite

“…In particular, Han et al [ 27 ], together with Chaunzwa et al [ 28 ], verified that the CNN model VGG-16 outperformed other conventional machine learning algorithms in terms of the classification and recognition accuracy of NSCLC on PET/CT images. In contrast, Bębas et al [ 29 ] identified NSCLC histological subtypes using PET/MRI texture analysis classification and achieved the best results (75.48%) among the many machine learning classifiers using support vector machines. However, these models only discriminate what type of lung cancer subtype is present, and there is room for expansion.…”

Section: Related Workmentioning

confidence: 99%

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

Zhan

Long

Gou

et al. 2021

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.

“…The simplest method is image histogram analysis (e.g., First-order Features). More sophisticated approaches additionally analyze local changes in pixel intensities (e.g., Gray-level Co-occurrence Matrix); others try to mimic the way the human visual system works (e.g., Gray Tone Difference Matrix) [ 31 , 32 ]. Since the texture operator has already proved its sensitiveness for tiny changes resulting from noise introduction [ 33 ], it is believed the pollution recorded on images may also be visible.…”

Section: Theorymentioning

confidence: 99%

Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter

Chuchro

Sarlej

Grzegorczyk

et al. 2021

The study was undertaken in Krakow, which is situated in Lesser Poland Voivodeship, where bad PM10 air-quality indicators occurred on more than 100 days in the years 2010–2019. Krakow has continuous air quality measurement in seven locations that are run by the Province Environmental Protection Inspectorate. The research aimed to create regression and classification models for PM10 and PM2.5 estimation based on sky photos and basic weather data. For this research, one short video with a resolution of 1920 × 1080 px was captured each day. From each film, only five frames were used, the information from which was averaged. Then, texture analysis was performed on each averaged photo frame. The results of the texture analysis were used in the regression and classification models. The regression models’ quality for the test datasets equals 0.85 and 0.73 for PM10 and 0.63 for PM2.5. The quality of each classification model differs (0.86 and 0.73 for PM10, and 0.80 for PM2.5). The obtained results show that the created classification models could be used in PM10 and PM2.5 air quality assessment. Moreover, the character of the obtained regression models indicates that their quality could be enhanced; thus, improved results could be obtained.