Using Deep Learning with Bayesian–Gaussian Inspired Convolutional Neural Architectural Search for Cancer Recognition and Classification from Histopathological Image Frames

Abstract

We propose a neural architectural search model which examines histopathological images to detect the presence of cancer in both lung and colon tissues. In recent times, deep artificial neural networks have made tremendous impacts in healthcare. However, obtaining an optimal artificial neural network model that could yield excellent performance during training, evaluation, and inferencing has been a bottleneck for researchers. Our method uses a Bayesian convolutional neural architectural search algorithm in collaboration with Gaussian processes to provide an efficient neural network architecture for efficient colon and lung cancer classification and recognition. The proposed model learns by using the Gaussian process to estimate the required optimal architectural values by choosing a set of model parameters through the exploitation of the expected improvement (EI) values, thereby minimizing the number of sampled trials and suggesting the best model architecture. Several experiments were conducted, and a landmark performance was obtained in both validation and test data through the evaluation of the proposed model on a dataset consisting of 25,000 images of five different classes with convergence and F1-score matrices.