Complementary Learning Fuzzy Neural Network Research Articles

A review of computer assisted detection/diagnosis (CAD) in breast thermography for breast cancer detection

Breast cancer is the leading type of cancer diagnosed in women. For years human limitations in interpreting the thermograms possessed a considerable challenge, but with the introduction of computer assisted detection/diagnosis (CAD), this problem has been addressed. This review paper compares different approaches based on neural networks and fuzzy systems which have been implemented in different CAD designs. The greatest improvement in CAD systems was achieved with a combination of fuzzy logic and artificial neural networks in the form of FALCON-AART complementary learning fuzzy neural network (CLFNN). With a CAD design based on FALCON-AART, it was possible to achieve an overall accuracy of near 90%. This confirms that CAD systems are indeed a valuable addition to the efforts for the diagnosis of breast cancer. Lower cost and high performance of new infrared systems combined with accurate CAD designs can promote the use of thermography in many breast cancer centres worldwide.

Ovarian cancer diagnosis with complementary learning fuzzy neural network

Early detection is paramount to reduce the high death rate of ovarian cancer. Unfortunately, current detection tool is not sensitive. New techniques such as deoxyribonucleic acid (DNA) micro-array and proteomics data are difficult to analyze due to high dimensionality, whereas conventional methods such as blood test are neither sensitive nor specific. Thus, a functional model of human pattern recognition known as complementary learning fuzzy neural network (CLFNN) is proposed to aid existing diagnosis methods. In contrast to conventional computational intelligence methods, CLFNN exploits the lateral inhibition between positive and negative samples. Moreover, it is equipped with autonomous rule generation facility. An example named fuzzy adaptive learning control network with another adaptive resonance theory (FALCON-AART) is used to illustrate the performance of CLFNN. The confluence of CLFNN-micro-array, CLFNN-blood test, and CLFNN-proteomics demonstrate good sensitivity and specificity in the experiments. The diagnosis decision is accurate and consistent. CLFNN also outperforms most of the conventional methods. This research work demonstrates that the confluence of CLFNN-DNA micro-array, CLFNN-blood tests, and CLFNN-proteomic test improves the diagnosis accuracy with higher consistency. CLFNN exhibits good performance in ovarian cancer diagnosis in general. Thus, CLFNN is a promising tool for clinical decision support.

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

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.