  Cervical cancer, one of the most common fatal cancers among women, can be
prevented by regular screening to detect any precancerous lesions at early
stages and treat them. Pap smear test is a widely performed screening technique
for early detection of cervical cancer, whereas this manual screening method
suffers from high false-positive results because of human errors. To improve
the manual screening practice, machine learning (ML) and deep learning (DL)
based computer-aided diagnostic (CAD) systems have been investigated widely to
classify cervical pap cells. Most of the existing researches require
pre-segmented images to obtain good classification results, whereas accurate
cervical cell segmentation is challenging because of cell clustering. Some
studies rely on handcrafted features, which cannot guarantee the classification
stage's optimality. Moreover, DL provides poor performance for a multiclass
classification task when there is an uneven distribution of data, which is
prevalent in the cervical cell dataset. This investigation has addressed those
limitations by proposing DeepCervix, a hybrid deep feature fusion (HDFF)
technique based on DL to classify the cervical cells accurately. Our proposed
method uses various DL models to capture more potential information to enhance
classification performance. Our proposed HDFF method is tested on the publicly
available SIPAKMED dataset and compared the performance with base DL models and
the LF method. For the SIPAKMED dataset, we have obtained the state-of-the-art
classification accuracy of 99.85%, 99.38%, and 99.14% for 2-class, 3-class, and
5-class classification. Moreover, our method is tested on the Herlev dataset
and achieves an accuracy of 98.32% for binary class and 90.32% for 7-class
classification.
