Classification of Breast Cancer Using a Hybrid and Enhanced Recurrent Residual Convolutional Neural Network (ERResCNN)

2021 ◽  
Vol 19 (2) ◽  
pp. 66-76
Author(s):  
S. Prakash ◽  
K. Sangeetha
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Normal Breast ◽  
World Health ◽  
Complex Task ◽  
Segmentation Methods ◽  
Health Organization ◽  
Prevention And Cure ◽  
Breast Tissues

Females are affected by BC (Breast Cancer) more than any other type of cancer. BC has caused more deaths than any other diseases such as tuberculosis or malaria according to WHO (World Health Organization). The mortality rates due to BC in women are high making it a candidate for early detection for prevention and cure. Diagnosing BC is a complex task as it is interleaved with normal breast tissues. Image processing methods have been proposed for detecting BC, yet better segmentation methods are required. Fuzzy based approaches provide optimal results in segmenting BC images. Hence, this work uses Fuzzy approach combined with ResCNN (Recurrent Residual Convolution Neural Network) which is the optimized by a modified GA (Genetic Algorithm). The proposed ERResCNN classifying results in detecting BC from images is accurate and efficient in comparison to other methods.

scholarly journals Automatic Prediction of Meningioma Grade Image Based on Data Amplification and Improved Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hong Zhu ◽  
Qianhao Fang ◽  
Hanzhi He ◽  
Junfeng Hu ◽  
Daihong Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Accuracy ◽  
World Health ◽  
Tumor Type ◽  
Grade Prediction ◽  
Health Organization ◽  
Deep Learning Model ◽  
The Brain

Meningioma is the second most commonly encountered tumor type in the brain. There are three grades of meningioma by the standards of the World Health Organization. Preoperative grade prediction of meningioma is extraordinarily important for clinical treatment planning and prognosis evaluation. In this paper, we present a new deep learning model for assisting automatic prediction of meningioma grades to reduce the recurrence of meningioma. Our model is based on an improved LeNet-5 model of convolutional neural network (CNN) and does not require the extraction of the diseased tissue, which can greatly enhance the efficiency. To address the issue of insufficient and unbalanced clinical data of meningioma images, we use an oversampling technique which allows us to considerably improve the accuracy of classification. Experiments on large clinical datasets show that our model can achieve quite high accuracy (i.e., as high as 83.33%) for the classification of meningioma images.

scholarly journals Analisis Klasifikasi Kanker Payudara Menggunakan Algoritma Naive Bayes

2020 ◽  
Vol 4 (3) ◽  
pp. 117
Author(s):  
Hardian Oktavianto ◽  
Rahman Puji Handri
Keyword(s):  
Breast Cancer ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
World Health ◽  
Average Percentage ◽  
Average Value ◽  
Treatment Measures ◽  
Bayes Algorithm ◽  
Health Organization

Breast cancer is one of the highest causes of death among women, this disease ranks second cause of death after lung cancer. According to the world health organization, 1 million women get a diagnosis of breast cancer every year and half of them die, in general this is due to early treatment and slow treatment resulting in new cancers being detected after entering the final stage. In the field of health and medicine, machine learning-based classification has been carried out to help doctors and health professionals in classifying the types of cancer, to determine which treatment measures should be performed. In this study breast cancer classification will be carried out using the Naive Bayes algorithm to group the types of cancer. The dataset used is from the Wisconsin breast cancer database. The results of this study are the ability of the Naive Bayes algorithm for the classification of breast cancer produces a good value, where the average percentage of correctly classified data reaches 96.9% and the average percentage of data is classified as incorrect only 3.1%. While the level of effectiveness of classification with naive bayes is high, where the average value of precision and recall is around 0.96. The highest precision and recall values are when the test data uses a percentage split of 40% with the respective values reaching 0.974 and 0.973.

scholarly journals Neuroendocrine carcinoma of the breast: a case report

2021 ◽  
pp. 306-311
Author(s):  
Iulia Gîvan ◽  
George Ciulei ◽  
Angela Cozma ◽  
Mădălina Indre ◽  
Vlad Țâru ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Manifestations ◽  
World Health ◽  
Neuroendocrine Neoplasms ◽  
Breast Carcinomas ◽  
Hormone Production ◽  
Carcinoma Of The Breast ◽  
Clinical Syndromes ◽  
Health Organization

Neuroendocrine breast carcinomas represent a rare subtype of breast cancer. Their definition, prevalence and prognosis remain controversial in the literature. Regarding the presentation, there are no differences from other breast carcinomas and clinical syndromes related to hormone production are extremely rare. Refinement of the classification of neuroendocrine neoplasms of the breast is needed in order to improve the reproducibility of their diagnostic criteria and to define their clinical significance. This article presents the case of a 44-year-old female patient diagnosed with invasive breast carcinoma with neuroendocrine features, according to the 2012 World Health Organization (WHO) definition, with focus on presentation, clinical manifestations, diagnostic approach and differential diagnosis.

scholarly journals The PCA and LDA Analysis on the Differential Expression of Proteins in Breast Cancer

2010 ◽  
Vol 29 (5) ◽  
pp. 231-242 ◽  
Author(s):  
Seng Liang ◽  
Manjit Singh ◽  
Saravanan Dharmaraj ◽  
Lay-Harn Gam
Keyword(s):  
Breast Cancer ◽  
Ductal Carcinoma ◽  
Protein Profile ◽  
Normal Breast ◽  
Tissue Samples ◽  
Proteomic Approach ◽  
Profile Changes ◽  
Isoform B ◽  
Breast Tissues

Breast cancer is a leading cause of mortality in women. In Malaysia, it is the most common cancer to affect women. The most common form of breast cancer is infiltrating ductal carcinoma (IDC). A proteomic approach was undertaken to identify protein profile changes between cancerous and normal breast tissues from 18 patients. Two protein extracts; aqueous soluble and membrane associated protein extracts were studied. Thirty four differentially expressed proteins were identified. The intensities of the proteins were used as variables in PCA and reduced data of six principal components (PC) were subjected to LDA in order to evaluate the potential of these proteins as collective biomarkers for breast cancer. The protein intensities of SEC13-like 1 (isoform b) and calreticulin contributed the most to the first PC while the protein intensities of fibrinogen beta chain precursor and ATP synthase D chain contributed the most to the second PC. Transthyretin precursor and apolipoprotein A-1 precursor contributed the most to the third PC. The results of LDA indicated good classification of samples into normal and cancerous types when the first 6 PCs were used as the variables. The percentage of correct classification was 91.7% for the originally grouped tissue samples and 88.9% for cross-validated samples.

scholarly journals Classification of Breast Cancer Malignancy Using Cytological Images of Fine Needle Aspiration Biopsies

2008 ◽  
Vol 18 (1) ◽  
pp. 75-83 ◽  
Author(s):  
Łukasz Jeleń ◽  
Thomas Fevens ◽  
Adam Krzyżak
Keyword(s):  
Breast Cancer ◽  
Fine Needle Aspiration ◽  
Needle Aspiration ◽  
World Health ◽  
Support Vector ◽  
Fine Needle ◽  
Malignancy Grade ◽  
Precise Diagnosis ◽  
Health Organization

Classification of Breast Cancer Malignancy Using Cytological Images of Fine Needle Aspiration BiopsiesAccording to the World Health Organization (WHO), breast cancer (BC) is one of the most deadly cancers diagnosed among middle-aged women. Precise diagnosis and prognosis are crucial to reduce the high death rate. In this paper we present a framework for automatic malignancy grading of fine needle aspiration biopsy tissue. The malignancy grade is one of the most important factors taken into consideration during the prediction of cancer behavior after the treatment. Our framework is based on a classification using Support Vector Machines (SVM). The SVMs presented here are able to assign a malignancy grade based on preextracted features with the accuracy up to 94.24%. We also show that SVMs performed best out of four tested classifiers.

Sixth Edition: the New Standard

2008 ◽  
Vol 13 (1) ◽  
pp. 1-12
Author(s):  
Christopher R. Brigham ◽  
Robert D. Rondinelli ◽  
Elizabeth Genovese ◽  
Craig Uejo ◽  
Marjorie Eskay-Auerbach
Keyword(s):  
Review Process ◽  
Peer Review Process ◽  
World Health ◽  
International Classification Of Functioning ◽  
Revision Process ◽  
Sixth Edition ◽  
Impairment Ratings ◽  
Health Organization ◽  
Impairment Severity

Abstract The AMA Guides to the Evaluation of Permanent Impairment (AMA Guides), Sixth Edition, was published in December 2007 and is the result of efforts to enhance the relevance of impairment ratings, improve internal consistency, promote precision, and simplify the rating process. The revision process was designed to address shortcomings and issues in previous editions and featured an open, well-defined, and tiered peer review process. The principles underlying the AMA Guides have not changed, but the sixth edition uses a modified conceptual framework based on the International Classification of Functioning, Disability, and Health (ICF), a comprehensive model of disablement developed by the World Health Organization. The ICF classifies domains that describe body functions and structures, activities, and participation; because an individual's functioning and disability occur in a context, the ICF includes a list of environmental factors to consider. The ICF classification uses five impairment classes that, in the sixth edition, were developed into diagnosis-based grids for each organ system. The grids use commonly accepted consensus-based criteria to classify most diagnoses into five classes of impairment severity (normal to very severe). A figure presents the structure of a typical diagnosis-based grid, which includes ranges of impairment ratings and greater clarity about choosing a discreet numerical value that reflects the impairment.

The Historical Development of Burden of Treatment Compliance

2014 ◽  
Vol 19 (5) ◽  
pp. 13-15
Author(s):  
Stephen L. Demeter
Keyword(s):  
Internal Medicine ◽  
Treatment Compliance ◽  
World Health ◽  
International Classification Of Functioning ◽  
Sixth Edition ◽  
Burden Of Treatment ◽  
Specific Goal ◽  
Surrogate Measure ◽  
Health Organization

Abstract A long-standing criticism of the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides) has been the inequity between the internal medicine ratings and the orthopedic ratings; in the comparison, internal medicine ratings appear inflated. A specific goal of the AMA Guides, Sixth Edition, was to diminish, where possible, those disparities. This led to the use of the International Classification of Functioning, Disability, and Health from the World Health Organization in the AMA Guides, Sixth Edition, including the addition of the burden of treatment compliance (BOTC). The BOTC originally was intended to allow rating internal medicine conditions using the types and numbers of medications as a surrogate measure of the severity of a condition when other, more traditional methods, did not exist or were insufficient. Internal medicine relies on step-wise escalation of treatment, and BOTC usefully provides an estimate of impairment based on the need to be compliant with treatment. Simplistically, the need to take more medications may indicate a greater impairment burden. BOTC is introduced in the first chapter of the AMA Guides, Sixth Edition, which clarifies that “BOTC refers to the impairment that results from adhering to a complex regimen of medications, testing, and/or procedures to achieve an objective, measurable, clinical improvement that would not occur, or potentially could be reversed, in the absence of compliance.

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