DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques

by   Md Mamunur Rahaman, et al.

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 5-class classification. Moreover, our method is tested on the Herlev dataset and achieves an accuracy of 98.32 classification.


page 3

page 7

page 9

page 13

page 16


From Human Mesenchymal Stromal Cells to Osteosarcoma Cells Classification by Deep Learning

Early diagnosis of cancer often allows for a more vast choice of therapy...

Urine Microscopic Image Dataset

Urinalysis is a standard diagnostic test to detect urinary system relate...

Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors

The Anti-Nuclear Antibody (ANA) clinical pathology test is commonly used...

Role of Class-specific Features in Various Classification Frameworks for Human Epithelial (HEp-2) Cell Images

The antinuclear antibody detection with human epithelial cells is a popu...

DeepPap: Deep Convolutional Networks for Cervical Cell Classification

Automation-assisted cervical screening via Pap smear or liquid-based cyt...

Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations

Colorectal cancer (CRC) is one of the most common types of cancer with a...

Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for Cervical Cytology Image Classification

Cervical cancer is the fourth most common category of cancer, affecting ...

Please sign up or login with your details

Forgot password? Click here to reset