Mutation based Atom Search Optimization Algorithm for Hyperparameter Optimization of the DenseNet121 Architecture for Cervical Cancer Classification

Abstract

The cervical cancer patient’s death rate can be minimized by accurate and early detection of cervical cancer (CC). One of the popular techniques called the Pap test or Pap smear is widely used for the early detection of CC. The manual analysis consumed more time in the case of CC detection. The existing techniques met few shortcomings in terms of poor accuracy, more computational complexity, higher feature dimensionality, poor reliability, and higher time-consumption with poor hyperparameters optimization. Hence, the computer-aided diagnostic model provides reliable and accurate CC detection at the initial stage. In this paper, we proposed MASO optimized DenseNet 121 architecture for the early detection of cervical cancer. At first, different kinds of augmentation techniques such as horizontal flip, vertical flip, zooming, shearing, height shift, width shift, rotation, and brightness to increase the number of training samples. The Mutation based Atom Search Optimization (MASO) algorithm is established to optimize the hyperparameters in DenseNet 121 architecture suchnumber of neurons in the dense layer, learning rate value, and the batch sizes. Different kinds of performance metrics such as accuracy, specificity, sensitivity, precisions, recall, F-score, and confusion matrix evaluate the performance of MASO optimized DenseNet 121 architecture for CC detection. A single normal class with three abnormal classes namely Carcinoma, Light dysplastic, and Sever dysplastic were selected from the Hervel dataset for experimental investigation. The proposed MASO optimized DenseNet 121 architecture achieves 98.38% accuracy, 98.5% specificity, 98.83% sensitivity, 98.58% precision, 99.3% recall and 98.25% F-score values than other existing methods.