Telemammography: A Novel Approach for Early Detection of Breast Cancer Through Wavelets Based Image Processing and Machine Learning Techniques

Telehealth monitoring is an innovative process of synergising the benefits of information and communication technologies (ICT) and Internet of Things (IoT) to deliver healthcare services to remote, distant, and underserved regions. The objective of this study is to deliver healthcare services to patients outside the conventional settings by connecting the patient and healthcare providers with technology. As technologies for telehealth monitoring have become more advanced, they have become fully integrated into delivery of healthcare service. One of the most publicized telehealth services is the use of telemammography in the early diagnosis of breast cancer from remote and rural locations. Automated detection and classification of tumor in telemammographic images is of high importance for physicians for accurate prediction of the diseases. This study presents advances in telehealth services and also proposes novel telemammography system for early detection of breast cancer from remote and underserved areas. In this study, we have used efficient wavelet-based image processing techniques for preprocessing, detection, and enhancing the resolution of mammographic images. A detailed comparative analysis is performed to select the best classification model using different classification algorithms. We have used Multi-Layer Perceptron Neural Networks, J48 decision trees, Random Forest, and K-Nearest Neighbor classifier for classifying the tumor into three categories namely: benign, malignant, and normal. The classification is based on the area, volume, and boundaries of tumor masses. All the tumor features and classification methods are compared using Accuracy, Sensitivity, Specificity, Precision, and Mean Square Error. Experimental results on the Mammographic Image Analysis Society (MIAS) database are found to give the best results when neural network classifier is used for classification of mammographic images.

[1]  Cristian Rotariu,et al.  Medical image processing by using soft computing methods and information fusion , 2011 .

[2]  Marilyn A Roubidoux,et al.  Update on new technologies in digital mammography , 2014, International journal of women's health.

[3]  Ehsan Sheybani,et al.  SURVEY OF TELEMEDICINE/TELERADIOLOGY/TELEMAMMOGRAPHY NETWORK ARCHITECTURES , 2001 .

[4]  Kesari Verma,et al.  Adaptive Gradient Descent Backpropagation for Classification of Breast Tumors in Ultrasound Imaging , 2015 .

[5]  Y Faridah,et al.  Biomedical Imaging and Intervention Journal Digital versus Screen Film Mammography: a Clinical Comparison , 2022 .

[6]  Sergios Theodoridis,et al.  A Novel Efficient Cluster-Based MLSE Equalizer for Satellite Communication Channels with-QAM Signaling , 2006, EURASIP J. Adv. Signal Process..

[7]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[8]  Marleen de Bruijne,et al.  Machine learning approaches in medical image analysis: From detection to diagnosis , 2016, Medical Image Anal..

[9]  Yi Guo,et al.  Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features , 2011, The open medical informatics journal.

[10]  A. Jemal,et al.  Breast Cancer Statistics , 2013 .

[11]  Passant Wahdan,et al.  Automated Breast Tumour Detection in Ultrasound Images Using Support Vector Machine and Ensemble Classification , 2016, Journal of Biomedical Engineering and Biosciences.

[12]  Kiran Kumari Patil,et al.  Digital telemammography services for rural India, software components and design protocol , 2014, 2014 International Conference on Advances in Electronics Computers and Communications.

[13]  C. N. Ravi Kumar,et al.  A Novel and Efficient Lifting Scheme based Super Resolution Reconstruction for Early Detection of Cancer in Low Resolution Mammogram Images , 2011 .

[14]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[15]  Subhasis Chaudhuri,et al.  Single-Frame Image Super-resolution through Contourlet Learning , 2006, EURASIP J. Adv. Signal Process..

[16]  Ehsan Sheybani.  ATMTN: A Test-Bed for a National Telemammography Network , 2002 .

[17]  J. Marc Overhage,et al.  Integration of radiographic images with an electronic medical record , 2001, AMIA.

[18]  S. Shapiro,et al.  Ten- to fourteen-year effect of screening on breast cancer mortality. , 1982, Journal of the National Cancer Institute.

[19]  Priti P. Rege,et al.  Fusion of local and global features for classification of abnormality in mammograms , 2016 .

[20]  Jean-Philippe Thiran,et al.  Interactive DICOM image transmission and telediagnosis over the European ATM network , 1998, IEEE Transactions on Information Technology in Biomedicine.

[21]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[22]  Wagner Coelho de Albuquerque Pereira,et al.  Breast tumor classification in ultrasound images using support vector machines and neural networks , 2016 .

[23]  K. Satya Prasad,et al.  AUTOMATIC DETECTION OF BREAST CANCER MASS IN MAMMOGRAMS USING MORPHOLOGICAL OPERATORS AND FUZZY C -MEANS CLUSTERING , 2009 .

[24]  K. Czajkowski,et al.  Teletransmission system supporting intensive insulin treatment of out-clinic type 1 diabetic pregnant women. Technical assessment during 3 years’ application , 2001, The International journal of artificial organs.

[25]  Sherin Sebastian,et al.  Remote Patient Monitoring System , 2012 .

[26]  Houjin Chen,et al.  A Survey of Computer-aided Detection of Breast Cancer with Mammography , 2016 .

[27]  Alexandros Labrinidis,et al.  Challenges and Opportunities with Big Data , 2012, Proc. VLDB Endow..