Towards designing a spatio-temporal neural network for non-modular high content pathological screening

• Published in: 2010 2nd International Conference on Computer Engineering and Technology Vol. 7; pp. V7-708 - V7-712
• Main Authors: Talware, Rajendra, Abhyankar, Aditya
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2010
• Subjects: cancer; cellular image analysis; Cellular networks; Cellular neural networks; evolutionary methodologies; Feature extraction; Image analysis; Image processing; Machine learning; Machine learning algorithms; Neural networks; Optimization methods; Pathology; wavelet based framework
• ISBN: 9781424463473; 1424463475
• DOI: 10.1109/ICCET.2010.5485698

Cellular image analysis is being attempted by several Image Processing (IP), statistical approaches and machine learning (ML), Neural Networks (NN) techniques. IP based algorithms are getting trapped into subjectivity due to reasons of cell image properties and methods of extractions and classification techniques. ML based methods are also well explored, but it suffer from large training sets and feature selection to reduce dimensionality of neural network. This paper proposes design and integration of Evolutionary methodologies with temporal networks and on-the-fly learning for getting desired confidence interval with minimal training. IP based method is experimented with morphological feature extraction. Results revels that, utility limited to spatial and noise-free data. This emphasizes the need of evolutionary methodologies with temporal learning and for optimizing weights, structure and learning of NN. Paper presents results extracted from IP algorithm with its shortcoming and proposes framework based on Evolutionary ANN to overcome the subjectivity issue reported in text and experimentation.