Cervical Cancer Diagnosis from Pap Smear: An Enhanced Extreme Learning Machine with Faster Training

Cervical cancer is one of the most commonly diagnosed cancers in women worldwide and remains deadly in low and middle income countries. As it grows slowly, it can be prevented if detected early through routine screening. The Pap smear is acknowledged globally as the definitive screening method for cervical cancer. However, classifying cervical cells from Pap smear images is challenging because of the complex morphological changes in cell structures. This paper proposes a deep learning framework with a cosine similarity-based extreme learning machine, enabling faster training and accurate classification of cervical pap cells. It has been observed that scarcity of data and uneven distribution of data among different classes lead to poor performance of a deep learning based multi-class classifier. To overcome these limitations, a hybrid model incorporating an auto-encoder and co-attention mechanism was used to augment data before feeding it in the proposed cosine similarity-based extreme learning machine (CS-ELM) classifier. The hybrid approach is evaluated on the publicly available SIPaKMeD and LBC datasets, and classification accuracies of [Formula: see text]% and [Formula: see text]% are achieved. The results indicate that, the model demonstrates enhanced effectiveness over current leading classifiers in the field of pap cell classification.