Classification of lung cancer with deep learning Res-U-Net and molecular imaging

被引:3
|
作者
Malligeswari, N. [1 ]
Kavya, G. [2 ]
机构
[1] SKR Engn Coll, Dept Elect & Commun Engn, Chennai, India
[2] SA Engn Coll, Dept Elect & Commun Engn, Chennai, India
关键词
Molecular imaging; Res-U-Net; SVM classifier; Small cell lung cancer; Non-small cell lung cancer; F-18-FDG PET/CT; LYMPHOMA; PET/CT;
D O I
10.1007/s11760-023-02635-7
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Lung cancer is a prevalent malignancy, despite the great breakthroughs in detection and prevention, and it remains the important cause of death. In recent days, artificial intelligence has exploded in all fields of science. The use of deep learning in medical science has improved in accuracy and precision of predicting this infestation in the initial stages. In the work, a novel molecular imaging-based Res-U-Net is proposed for classifying two different types of lung cancer. The PET/CT (positron emission tomography/computed tomography) employing an injection F-18-FDG has developed as a useful tool in therapeutic oncologic imaging for both metabolic and anatomic analysis. The proposed model uses Res-U-Net to classify small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) from normal by using F-18-FDG PET/CT images from the radiogenomics dataset. This dataset images are pre-processed by Gaussian smoothing to reduce the noise from the PET/CT images. Finally, the classification result is obtained through the support vector machine (SVM) classifier which proves the efficiency of the proposed technique. The outcome of the proposed technique yields the best and most accurate results, and it yields the classification accuracy rate of 96.45%for lung cancer into NSCLC and SCLC.
引用
收藏
页码:325 / 333
页数:9
相关论文
共 50 条
  • [41] POI Detection of High-Rise Buildings Using Remote Sensing Images: A Semantic Segmentation Method Based on Multitask Attention Res-U-Net
    Li, Bingnan
    Gao, Jiuchong
    Chen, Shuiping
    Lim, Samsung
    Jiang, Hai
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [42] SPF-Net: Solar panel fault detection using U-Net based deep learning image classification
    Rudro, Rifat Al Mamun
    Nur, Kamruddin
    Al Sohan, Md. Faruk Abdullah
    Mridha, M. F.
    Alfarhood, Sultan
    Safran, Mejdl
    Kanagarathinam, Karthick
    ENERGY REPORTS, 2024, 12 : 1580 - 1594
  • [43] Transfer Learning with Res2Net for Remote Sensing Scene Classification
    Das, Arijit
    Chandran, Saravanan
    2021 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2021), 2021, : 796 - 801
  • [44] Application of Genetic Algorithm and U-Net in Brain Tumor Segmentation and Classification: A Deep Learning Approach
    Arif, Muhammad
    Jims, Anupama
    Ajesh, F.
    Geman, Oana
    Craciun, Maria-Daniela
    Leuciuc, Florin
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [45] Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging
    Khalil, Saman
    Nawaz, Uroosa
    Zubariah, Zohaib
    Mushtaq, Zohaib
    Arif, Saad
    Rehman, Muhammad Zia Ur
    Qureshi, Muhammad Farrukh
    Malik, Abdul
    Aleid, Adham
    Alhussaini, Khalid
    APPLIED SCIENCES-BASEL, 2023, 13 (07):
  • [46] Lung Nodule Segmentation and Classification using U-Net and Efficient-Net
    Suriyavarman, S.
    Annie, R. Arockia Xavier
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (07) : 737 - 745
  • [47] Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks
    Sevakula, Rahul K.
    Singh, Vikas
    Verma, Nishchal K.
    Kumar, Chandan
    Cui, Yan
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2019, 16 (06) : 2089 - 2100
  • [48] Deep Learning-Based Conversion of Phased Array Ultrasonic Imaging using U-Net
    Park, Keonhyeok
    Park, Choon-Su
    Park, Jun Hyeong
    Lee, Hyung Jin
    Lee, Seungchul
    JOURNAL OF THE KOREAN SOCIETY FOR NONDESTRUCTIVE TESTING, 2023, 43 (04) : 285 - 291
  • [49] Deep Learning Based Model Observer by U-Net
    Lorente, Iris
    Abbey, Craig
    Brankov, Jovan G.
    MEDICAL IMAGING 2020: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, 2020, 11316
  • [50] Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection
    Naseer, Iftikhar
    Akram, Sheeraz
    Masood, Tehreem
    Rashid, Muhammad
    Jaffar, Arfan
    IEEE ACCESS, 2023, 11 : 60279 - 60291