A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure

Abstract

Early detection of breast cancer is the key to improve survival rate. Thermogram is a promising front-line screening tool as it is able to warn women of breast cancer up to 10 years in advance. However, analysis and interpretation of thermogram are heavily dependent on the analysts, which may be inconsistent and error-prone. In order to boost the accuracy of preliminary screening using thermogram without incurring additional financial burden, Complementary Learning Fuzzy Neural Network (CLFNN), FALCON-AART is proposed as the Computer-Assisted Intervention (CAI) tool for thermogram analysis. CLFNN is a neuroscience-inspired technique that provides intuitive fuzzy rules, human-like reasoning, and good classification performance. Confluence of thermogram and CLFNN offers a promising tool for fighting breast cancer.

Automatic Intelligent Asymmetry Detection Using Digital Infrared Imaging with K-Means Clustering

“…Since cancer cells with their higher metabolic rate are hotter than normal cells, which makes cancerous tumors appear as hotspots in DIT images, this technique is particularly useful in female breast | 180 imaging [13]. While higher-order statistics such as variance, skewness, kurtosis, and joint entropy are effective measures of asymmetry [13], there are other technical methodologies to develop an automatic asymmetry analysis system, such as independent component analysis [12], Hough transform-aided image segmentation and pattern classification [16], and fuzzy logic [17].…”

Application of infrared thermography in computer aided diagnosis

“…Even back in 1973, it was established that the combination of thermography and mammography achieves better results, for diagnosing asymptomatic breast cancer, then each method individually [240]. However, the interpretation of thermograms is heavily dependent on the analysts, which may be inconsistent and error-prone [89]. Therefore, breast cancer screening with IR imaging has still a weaker position [241].…”

“…Receiver Operating Characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. A wide range of studies on IR thermography used the ROC curve to measure the cutoff point, diagnostic accuracy (indicated by the area under the curve), sensitivity, and specificity [67,89]. Despite these positive characteristics, Cook points out that ROC measures may be mediocre in assessing models that predict future risk or stratify individuals into risk categories [90].…”

Classification of Breast Masses based on Cognitive Resonance

“…Although capable of achieving an optimal performance, developing an ANN-based classifier would be rather time-consuming since it may take a few hundreds to thousands of runs before figuring out the appropriate parameters [7]. Moreover, statistical methods are difficult to develop, and they often work under the assumption that the underlying data are normally distributed [7].…”

“…Despite technology advances in the fields of mammography [2]- [6], thermography [7], optical tomography [8] and other anticancer methodologies in the last two decades, breast cancer is still a prominent problem. Early detection of breast cancer increases the survival rate as well as the treatment options [2].…”