Deep learning-based ovarian cyst classification and abnormality detection using convolutional neural networks

被引:0
|
作者
Munish Sood [1 ]
Emjee Puthooran [1 ]
Nishant Jain [1 ]
机构
[1] JUIT,Department of Electronics and Communication Engineering
关键词
Ovarian cysts; Deep learning; Transfer learning; OvarianCystNet; VGG16; VGG19; Resnet50; ADAM;
D O I
10.1007/s00521-024-10810-1
中图分类号
学科分类号
摘要
The goal of this study was to design a computer-aided diagnosis system using deep learning network for ovarian cyst classification and abnormality detection. An ovarian cyst is a sac filled with fluid or semisolid material that forms on or within one or both the ovaries. One in every 4–5 women in India suffers from different types of ovarian cysts (20–25% incidence). The doctor-population ratio of 0.62:1000 further worsens this situation for the most populous country on the planet. The main motivation for this work was to ease this situation by providing an automated system which can act as an aid to doctors in diagnosing ovarian cysts for different complications requiring immediate medical intervention. It can also be used as teaching aid for the gynecology/radiology students to identify the sonographic variations being exhibited by different ovarian cyst types. A new deep learning convolutional neural network dedicated for Ovarian cysts classification and abnormality detection, OvarianCystNet with 87 layers is designed using Deep Learning Toolbox of MATLAB R 2022b. Most of deep learning ovarian cyst classification systems are either using only pre-trained networks like VGG16 or inception V3. We have designed a dedicated deep learning network specifically for ovarian cyst classification. The classification results for OvarianCystNet show promising results and validate its use as tool for ovarian cyst diagnosis. A validation accuracy of 92.06% is achieved. It is 18.68% more accurate in comparison to VGG19, 7.65% more accurate in comparison with VGG16, 3.47% more accurate in comparison with MobileNetV2 and 1.49% more accurate in comparison with Resnet50. The training accuracy, validation loss, confusion matrix, receiver operating characteristics, and area under the curve is compared with four pre-trained deep learning networks namely MobilenetV2, Resnet50, VGG16 and VGG19. A dataset consisting of around 1319 images of ovarian cyst ultrasound images was collected from a leading obstetrics and gynecological hospital by several operators on Voluson-S8 a 4D ultrasound machine. Voluson-S8 is manufactured by general electric which is specifically used for obstetrics and gynecology related scans. All images in the dataset were manually labeled by an expert obstetrics and gynecology doctor with over 15 years of experience. Images were divided into five classes.
引用
收藏
页码:3047 / 3059
页数:12
相关论文
共 50 条
  • [41] A New Method Based on Deep Convolutional Neural Networks for Object Detection and Classification
    Yan Liu
    Zhu Zhuxngjie
    Zhang, Qiuhui
    Ding, Xiaotian
    Wang, Ruonan
    Han, Senyao
    Chi Li
    AATCC JOURNAL OF RESEARCH, 2021, 8 : 37 - 45
  • [42] Multi-Label Cardiac Abnormality Classification from Electrocardiogram Using Deep Convolutional Neural Networks
    Wickramasinghe, Nima L.
    Athif, Mohamed
    2021 COMPUTING IN CARDIOLOGY (CINC), 2021,
  • [43] Deep Learning-Based Classification for Melanoma Detection Using XceptionNet
    Lu, Xinrong
    Zadeh, Y. A. Firoozeh Abolhasani
    JOURNAL OF HEALTHCARE ENGINEERING, 2022, 2022
  • [44] Malware Classification using Deep Convolutional Neural Networks
    Kornish, David
    Geary, Justin
    Sansing, Victor
    Ezekiel, Soundararajan
    Pearlstein, Larry
    Njilla, Laurent
    2018 IEEE APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP (AIPR), 2018,
  • [45] Flower classification using deep convolutional neural networks
    Hiary, Hazem
    Saadeh, Heba
    Saadeh, Maha
    Yaqub, Mohammad
    IET COMPUTER VISION, 2018, 12 (06) : 855 - 862
  • [46] Gas Classification Using Deep Convolutional Neural Networks
    Peng, Pai
    Zhao, Xiaojin
    Pan, Xiaofang
    Ye, Wenbin
    SENSORS, 2018, 18 (01)
  • [47] Deep Learning Based Detection of Dermatological Diseases Using Convolutional Neural Networks and Decision Trees
    Mulani, Altaf O.
    Birajadar, Ganesh
    Ivkovic, Nikola
    Salah, Bashir
    Darlis, Arsyad R.
    TRAITEMENT DU SIGNAL, 2023, 40 (06) : 2819 - 2825
  • [48] A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks
    Alalharith, Dima M.
    Alharthi, Hajar M.
    Alghamdi, Wejdan M.
    Alsenbel, Yasmine M.
    Aslam, Nida
    Khan, Irfan Ullah
    Shahin, Suliman Y.
    Dianiskova, Simona
    Alhareky, Muhanad S.
    Barouch, Kasumi K.
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2020, 17 (22) : 1 - 10
  • [49] EEG Signals to Digit Classification Using Deep Learning-Based One-Dimensional Convolutional Neural Network
    Smita Tiwari
    Shivani Goel
    Arpit Bhardwaj
    Arabian Journal for Science and Engineering, 2023, 48 : 9675 - 9691
  • [50] Automated Classification of Breast Cancer Histology Images Using Deep Learning Based Convolutional Neural Networks
    Nawaz, Majid Ali
    Sewissy, Adel A.
    Soliman, Taysir Hassan A.
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2018, 18 (04): : 152 - 160