![]() ![]() In general, any ANN consists of an input layer, a hidden layer, and an output layer (illustrated in Fig. 1989) which is inspired by the biological neuron-an essential part of the biological neural networks. The ANN comprises a fundamental unit known as perceptron (Hornik et al. The ML algorithms such as support vector machine (SVM), artificial neural network (ANN), and cluster analysis are commonly implemented methods among which ANNs have proved to be very effective in addressing classification problems (LeCun et al. These methods aid in the identification of significant features during training of the ML algorithm, which may later be utilized for the classification or prediction of the test data set (Zhang, 2017). Machine learning (ML) is one such area of research, which performs statistical learning with the help of various multivariate analytical methods such as independent component analysis, principal component analysis (PCA), and multivariate regression. To overcome these limitations, an automatic, fast, and robust image analysis technique that delivers well-processed images with defined image quality criteria is desirable (Rivenson et al. Besides, the manual analysis is highly subjective. Manual image analysis of tissue samples is a very tedious and time-consuming process due to the complex nature of biological entities, which in turn demands an expert pathologist to record an accurate output. However, modern optical imaging techniques that are label-free or employ exogenous labels such as wide-field microscopy, phase contrast microscopy, fluorescence microscopy, and nonlinear optical microscopy may be limited by the quality of output image, post image processing, or instrumentation cost (Mazumder et al. Biophotonics is an interdisciplinary field with flourishing applications in biomedical research, which allows for the delivery of clear insights regarding complex biological systems through the interaction of light with biological samples. ![]()
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