scholarly journals Classification of Pap smear images for Cervical cancer using Convolutional Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 2801-2807
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Convolutional Neural Network ◽  
Pap Smear ◽  
Severe Dysplasia ◽  
Normal Cells ◽  
Second Stage ◽  
Two Stages ◽  
Cancerous Cells

Classification of Pap smear images for cervical cancer consists of two types namely, normal and abnormal cancerous cells. The dataset involves 7 sets of classes of cancerous images which have 3 sets of normal cancerous images and 4 sets of abnormal cancerous images. The proposed work performs two stages of classification. The first stage of the work is classifying the data as normal or abnormal cancerous cells. In the second stage of the work, the class of the cancer as normal columnar, normal intermediate, normal superficial, light dysplasia, moderate dysplasia, severe dysplasia and carcinoma_in_situ are classified. The proposed work gives good results for classifying images for 3 sets of classes and 4 sets of classes for normal cells and is able to classify and detect normal and abnormal cell accurately.

scholarly journals Cervical cancer detection method using an improved cellular neural network (CNN) algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 210 ◽  
Author(s):  
Azian Azamimi Abdullah ◽  
Aafion Fonetta Dickson Giong ◽  
Nik Adilah Hanin Zahri
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Cancer Detection ◽  
Pap Smear ◽  
Cellular Neural Network ◽  
Pap Smear Test ◽  
Abnormal Cells ◽  
Computer Based ◽  
Low Sensitivity ◽  
Cancerous Cells

<span>Cervical cancer is the second most common in Malaysia and the fourth frequent cancer among women in worldwide.  Pap smear test is often ignored although it is actually useful, beneficial and essential as screening tool for cervical cancer. However, Pap smear images have low sensitivity as well as specificity. Therefore, it is difficult to determine whether the abnormal cells are cancerous or not. Recently, computer-based algorithms are widely used in cervical cancer screening. In this study, an improved cellular neural network (CNN) algorithm is proposed as the solution to detect the cancerous cells in real-time by undergoing the image processing of Pap smear images. A few templates are combined and modified to form an ideal CNN algorithm to detect the cancerous cells in total of 115 Pap smear images. A MATLAB based CNN is developed for an automated detection of cervix cancerous cells where the templates segmented the nucleus of the cells. From the simulation results, our proposed CNN algorithm can detect the cervix cancer cells automatically with more than 88% accuracy.</span>

scholarly journals KLASIFIKASI TIPE SEL NORMAL/ABNORMAL BERDASARKAN CITRA PAP-SMEAR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol 6 (1) ◽  
pp. 35-41
Author(s):  
Muhamad Fathurahman ◽  
Rachmadhani Ajeng Nurmufti ◽  
Elan Suherlan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Local Binary Pattern ◽  
Pap Smear ◽  
Cell Types ◽  
Cancer Type ◽  
Manual Analysis ◽  
Testing Data ◽  
Occurrence Matrix

The classification of cell types plays an essential role in monitoring the growth of cancer cells. One of the methods to determine the cancer type is to analyze the pap-smear images manually. Nevertheless, the manual analysis of pap-smear images by the expert has several limitations, such as time-consuming and prone to misdiagnosis. For reducing the risks, it requires the automatic classification of cell types based on pap-smear images. This study utilizes the convolutional neural network (CNN) architectures to automatically classify the cell type into two-class categories (normal/abnormal) based on three features. These features, such as the local binary pattern, gray level co-occurrence matrix, and shape features, are extracted from pap-smear images. This study shows the performance of CNN achieved the maximum accuracy of 99.98%, 100.0%, 99.78% in training, validation, and testing data. Our approach also outperforms the performance of the baseline methods.    Keywords : CNN, Classification, Cell, Neural Network, Pap-smear

scholarly journals DeepFrag-k: a fragment-based deep learning approach for protein fold recognition

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Wessam Elhefnawy ◽  
Min Li ◽  
Jianxin Wang ◽  
Yaohang Li
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Fold Recognition ◽  
Major Protein ◽  
Learning Approach ◽  
Protein Fold ◽  
Protein Fold Recognition ◽  
Second Stage ◽  
Two Stages

Abstract Background One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolutional neural network (CNN) to classify the fragment vector into the corresponding fold. Results Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition. Conclusions There is a set of fragments that can serve as structural “keywords” distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition.

scholarly journals Adaptive Pruning of Transfer Learned Deep Convolutional Neural Network for Classification of Cervical Pap Smear Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 50674-50683 ◽  
Author(s):  
Pin Wang ◽  
Jiaxin Wang ◽  
Yongming Li ◽  
Linyu Li ◽  
Hehua Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pap Smear ◽  
Deep Convolutional Neural Network

scholarly journals Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images.

2017 ◽  
Vol 114 ◽  
pp. 281-287 ◽  
Author(s):  
Haidar A. Almubarak ◽  
R. Joe Stanley ◽  
Rodney Long ◽  
Sameer Antani ◽  
George Thoma ◽  
...  
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Convolutional Neural Network ◽  
Uterine Cervical Cancer

A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images

2020 ◽  
Vol 107 ◽  
pp. 101897
Author(s):  
Elima Hussain ◽  
Lipi B. Mahanta ◽  
Chandana Ray Das ◽  
Manjula Choudhury ◽  
Manish Chowdhury
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pap Smear ◽  
Shape Context

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

Sign in / Sign up

Export Citation Format

Share Document