Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

Abstract

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.

The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

“…Moreover, only the SumEntrp and Entropy for the red component in ROI 1, differed between all three bodyweight ratio groups, 11.2%, 14.2%, and 16.9%. These entropy-related features have been suggested to reflect the increased degree of thermal energy dissipation [ 25 , 31 , 40 ], indicating a high heterogeneity of texture [ 26 , 60 ].…”

“…Such assessment, following Dyson et al [ 30 ], should be useful in the everyday equine practice, where a horse performs the under-rider overground exercise with turns and circles. As the practical application of modern technologies is developing dynamically both in the field of equine thermal imaging [ 62 , 63 , 64 ] and DIP of medical images [ 31 , 33 , 36 , 37 , 38 , 39 , 40 ], each new approach can ultimately reach every horse owner.…”

“…IRT produces colorful thermal images, where the color gradient corresponds to the emitted infrared radiation and thus to the distribution of surface body temperatures [ 21 ]. Conventional thermal features, such as average, maximal, or minimal temperature, reflect one numerical value each for considered regions of interest (ROIs), whereas IRT images of the body surface, are composed of many pixels that reflect the body’s complex response [ 22 , 25 , 31 ]. As the pixel relations are assessed by image texture analysis, histogram statistics (HS), gray-level run-length matrix (GLRLM) and gray level co-occurrence matrix (GLCM) approaches have been applied in the detailed evaluation of medical images.…”

“…The texture operators explore the image by using statistics of pixel distribution to provide complex descriptions of image texture [ 32 , 33 ]. Consequently, texture analyses have successfully been used in advanced diagnostic imaging to improve the richness of information from ultrasound images [ 34 ], radiographic images [ 35 , 36 , 37 ], magnetic resonance images [ 33 , 38 ], and thermal images [ 25 , 31 , 39 , 40 ].…”

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

“…The GLM has been recently applied in equine medicine to detect the influence of a rider’s body weight on the thermography of the thoracolumbar region [ 31 ]. Moreover, GLM has successfully been used in human medicine to improve the extraction of texture features of ultrasound images [ 30 ], radiographic images [ 42 ], magnetic resonance images [ 29 , 43 ], and thermal images [ 27 , 44 , 45 , 46 , 47 , 48 ]. GLM describes a group of texture operators that map image function, image complexity, and statistics of pixel distribution [ 28 ] and has been reported to provide complex descriptions of image texture [ 28 , 29 , 43 , 44 ].…”