Journal ArticleDOI
A novel classification scheme to decline the mortality rate among women due to breast tumor.
TLDR
A robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics is presented and it is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.Abstract:
Early screening of skeptical masses or breast carcinomas in mammograms is supposed to decline the mortality rate among women. This amount can be decreased more on development of the computer-aided diagnosis with reduction of false suppositions in medical informatics. Our aim is to provide a robust tumor detection system for accurate classification of breast masses using normal, abnormal, benign, or malignant classes. The breast carcinomas are classified on the basis of observed classes. This is highly dependent on feature extraction process. In propose work, a novel algorithm for classification based on the combination of top Hat transformation and gray level co-occurrence matrix with back propagation neural network. The aim of this study is to present a robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics. The proposed method is verified on two datasets MIAS and DDSM. It is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.read more
Citations
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Journal ArticleDOI
Classification of acute lymphoblastic leukemia using deep learning
TL;DR: A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results, and experimental results reveal that the proposed method achieved 97.78% accuracy.
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Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture
TL;DR: A new deep learning‐based method is proposed for microscopic brain tumor detection and tumor type classification and a comparison with existing techniques shows the proposed design yields comparable accuracy.
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Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction
TL;DR: This work proposes a new automated approach for skin lesion detection and recognition using a deep convolutional neural network (DCNN) and concludes that the proposed method outperforms several existing methods and attained accuracy 98.4% on PH2 dataset, 95.1% on ISBI dataset and 94.8% onISBI 2017 dataset.
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Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges.
TL;DR: The study highlights how cancer diagnosis, cure process is assisted using machine learning with supervised, unsupervised and deep learning techniques.
Journal ArticleDOI
Feature enhancement framework for brain tumor segmentation and classification
Bilal Tahir,Sajid Iqbal,Sajid Iqbal,M. Usman Ghani Khan,Tanzila Saba,Zahid Mehmood,Adeel Anjum,Toqeer Mahmood +7 more
TL;DR: Experimental results show that application of proper preprocessing techniques could improve the classification and segmentation results to a greater extent, however, the combinations of these techniques depend on the characteristics and type of data set used.
References
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Journal ArticleDOI
International Variation in Female Breast Cancer Incidence and Mortality Rates
Carol DeSantis,Freddie Bray,Jacques Ferlay,Joannie Lortet-Tieulent,Benjamin O. Anderson,Ahmedin Jemal +5 more
TL;DR: Global trends in female breast cancer rates are decreasing in most high-income countries, despite increasing or stable incidence rates, and the increasing incidence and mortality rates in a number of countries are of concern, particularly those undergoing rapid changes in human development.
Journal ArticleDOI
Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images
Berkman Sahiner,Heang Ping Chan,Nicholas Petrick,Datong Wei,Mark A. Helvie,Dorit D. Adler,Mitchell M. Goodsitt +6 more
TL;DR: The authors' results demonstrate the feasibility of using a convolution neural network for classification of masses and normal tissue on mammograms using a generalized, fast and stable implementation of the CNN.
Journal ArticleDOI
Measures of acutance and shape for classification of breast tumors
TL;DR: A region-based measure of image edge profile acutance is proposed which characterizes the transition in density of a region of interest (ROI) along normals to the ROI at every boundary pixel and indicates the importance of including lesion edge definition with shape information for classification of tumors.
Journal ArticleDOI
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review.
Afsaneh Jalalian,Syamsiah Mashohor,Hajjah Rozi Mahmud,M. Iqbal Saripan,Abdul Rahman Ramli,Babak Karasfi +5 more
TL;DR: The approaches which are applied to develop CAD systems on mammography and ultrasound images are presented and the performance evaluation metrics of CAD systems are reviewed.
Journal Article
Use of Artificial Neural Network in Pattern Recognition
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.