OBJECTIVE:Automated Pap smear cervical screening is one of the most effective imaging based cancer detection
tools used for categorizing cervical cell images as normal and abnormal. Traditional classification methods depend on
hand-engineered features and show limitations in large, diverse datasets. Effective feature extraction requires an efficient
image preprocessing and segmentation, which remains prominent challenge in the field of Pathology. In this paper, a
deep learning concept is used for cell image classification in large datasets. METHODS:This relatively proposed novel
method, combines abstract and complicated representations of data acquired in a hierarchical architecture. Convolution
Neural Network (CNN) learns meaningful kernels that simulate the extraction of visual features such as edges, size,
shape and colors in image classification. A deep prediction model is built using such a CNN network to classify the
various grades of cancer: normal, mild, moderate, severe and carcinoma. It is an effective computational model which
uses multiple processing layers to learn complex features. A large dataset is prepared for this study by systematically
augmenting the images in Herlev dataset. RESULT:Among the three sets considered for the study, the first set of single
cell enhanced original images achieved an accuracy of 94.1% for 5 class, 96.2% for 4 class, 94.8% for 3 class and
95.7% for 2 class problems. The second set includes contour extracted images showed an accuracy of 92.14%, 92.9%,
94.7% and 89.9% for 5, 4, 3 and 2 class problems. The third set of binary images showed 85.07% for 5 class, 84%
for 4 class, 92.07% for 3 class and highest accuracy of 99.97% for 2 class problems. CONCLUSION:The experimental
results of the proposed model showed an effective classification of different grades of cancer in cervical cell images,
exhibiting the extensive potential of deep learning in Pap smear cell image classification.
tools used for categorizing cervical cell images as normal and abnormal. Traditional classification methods depend on
hand-engineered features and show limitations in large, diverse datasets. Effective feature extraction requires an efficient
image preprocessing and segmentation, which remains prominent challenge in the field of Pathology. In this paper, a
deep learning concept is used for cell image classification in large datasets. METHODS:This relatively proposed novel
method, combines abstract and complicated representations of data acquired in a hierarchical architecture. Convolution
Neural Network (CNN) learns meaningful kernels that simulate the extraction of visual features such as edges, size,
shape and colors in image classification. A deep prediction model is built using such a CNN network to classify the
various grades of cancer: normal, mild, moderate, severe and carcinoma. It is an effective computational model which
uses multiple processing layers to learn complex features. A large dataset is prepared for this study by systematically
augmenting the images in Herlev dataset. RESULT:Among the three sets considered for the study, the first set of single
cell enhanced original images achieved an accuracy of 94.1% for 5 class, 96.2% for 4 class, 94.8% for 3 class and
95.7% for 2 class problems. The second set includes contour extracted images showed an accuracy of 92.14%, 92.9%,
94.7% and 89.9% for 5, 4, 3 and 2 class problems. The third set of binary images showed 85.07% for 5 class, 84%
for 4 class, 92.07% for 3 class and highest accuracy of 99.97% for 2 class problems. CONCLUSION:The experimental
results of the proposed model showed an effective classification of different grades of cancer in cervical cell images,
exhibiting the extensive potential of deep learning in Pap smear cell image classification.
