RESUMEN
HIGHLIGHTS Novel whale optimization algorithm is proposed for prediction of breast cancer. Deep learning-based WOA adjusts the CNN structure as per maximum detection accuracy. Proposed method achieves 92.4% accuracy in comparison to 90.3%. Validity of method is evaluated with magnifying factors like 40x, 100 x, 200x, 400x.
Abstract Breast cancer is one of the most common cancers among women that cause billions of deaths worldwide. Identification of breast cancer often depends on the examination of digital biomedical photography such as the histopathological images of various health professionals, and clinicians. Analyzing histopathological images is a unique task and always requires special knowledge to conclude investigating these types of images. In this paper, a novel efficient technique has been proposed for the detection and prediction of breast cancer at its early stage. Initially, the dataset of images is used to carry out the pre-processing phase, which helps to transform a human pictorial image into a computer photographic image and adjust the parameters appropriate to the Convolutional neural network (CNN) classifier. Afterward, all the transformed images are assigned to the CNN classifier for the training process. CNN classifies incoming breast cancer clinical images as malignant and benign without prior information about the occurrence of cancer. For parameter optimization of CNN, a deep learning-based whale optimization algorithm (WOA) has been proposed which proficiently and automatically adjusts the CNN network structure by maximizing the detection accuracy. We have also compared the obtained accuracy of the proposed algorithm with a standard CNN and other existing classifiers and it is found that the proposed algorithm supersedes the other existing algorithms.