Optimized attention Induced multi head convolutional neural network with Densenet201 for cervical cancer diagnosis

Abstract

Cervical cancer is the fourth most common disease globally, highlighting the importance of early detection for effective treatment. Although the Pap smear test is the gold standard for detecting cervical cancer, its effectiveness depends on the expertise and dedication of the physicians. In this paper, Optimized Attention Induced Multi Head Convolutional Neural Network with Densenet201 for Cervical Cancer Diagnosis (AIMHCNN-Densenet201-CCD) is proposed. Initially, the input images are gathered from SIPaKMeD and Medical Scan Classification Dataset. Then, the input data is pre-processed using Regularized Bias-aware Ensemble Kalman filter (RBEKF) to crop and rotate input images. The pre-processed images are fed to Modified Spline-Kernelled Chirplet Transform (MSKCT) to extract the morphological features such as Shape, Colour, Structure and Size. Afterwards, the extracted features are fed into Multi-Head Convolutional Neural Network with Attention Induced and Densenet201 (AIMHCNN-Densenet201) for diagnosing Cervical Cancer like Dyskeratotic, Metaplastic, Koilocytotic, Parabasal and Superficial-Intermediate. Finally, Dove Swarm Optimization (DSO) is proposed to optimize the weight parameter of AIMHCNN-Densenet201 classifier that precisely diagnoses the Cervical Cancer. The proposed AIMHCNN-Densenet201-CCD method is implemented and analyzed using performance metrics such as accuracy, precision, specificity, f1-score, sensitivity, error rate and computation time. The proposed approach attains 29.82 %, 21.24 %, 18.97 % higher accuracy and 24.75 %, 32.57 %, and 29.69 % higher precision compared with existing methods respectively.