• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3_1spc
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• pp.496-503
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• 2013

Recently, the death rate owing to breast cancers has been increasing, and the occurrence age for breast cancers is lowering every year. Mammography is known to be a reliable detection method for breast cancers and works by detecting texture changes, calcifications, and other potential symptoms. In this research on breast cancer detection, candidate objects were detected by using image processing on mammograms, and feature analysis was used to classify candidate objects as benign tumors and malignant tumors. To find candidate objects, image pre-processing and binarization using multiple thresholds, and the grouping of micro-calcifications were used. More than 50 shape features and intensity features were used in the classification. The performance of the detection algorithm by using Euclidian distance method for benign tumors was 93%, and the classification error rate was approximately 2%.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.565-579
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• 2016

In this paper a novel cancer subtype's classifier based on Hybrid Samples Balanced Genetic Algorithm with Extreme Learning Machine (hSBGA-ELM) is presented. Proposed cancer subtype's classifier uses genes' expression data of 16063 genes from open Global Cancer Map (GCM) data base for accurate cancer subtype's classification. Proposed method efficiently classifies 14 subtypes of cancer (breast, prostate, lung, colorectal, lymphoma, bladder, melanoma, uterus, leukemia, renal, pancreas, ovary, mesothelioma and CNS). Proposed hSBGA-ELM unifies genes' selection procedure and cancer subtype's classification into one framework. Proposed Hybrid Samples Balanced Genetic Algorithm searches a reduced robust set of genes responsible for cancer subtype's classification from 16063 genes available in GCM data base. Selected reduced set of genes is used to build cancer subtype's classifier using Extreme Learning Machine (ELM). As a result, reduced set of robust genes guarantees stable generalization performance of the proposed cancer subtype's classifier. Proposed hSBGA-ELM discovers 95 genes probably responsible for cancer. Comparison with existing cancer subtype's classifiers clear indicates efficiency of the proposed method.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.113-120
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• 2023

One of the most prevalent disease among women that leads to death is breast cancer. It can be diagnosed by classifying tumors. There are two different types of tumors i.e: malignant and benign tumors. Physicians need a reliable diagnosis procedure to distinguish between these tumors. However, generally it is very difficult to distinguish tumors even by the experts. Thus, automation of diagnostic system is needed for diagnosing tumors. This paper attempts to improve the accuracy of breast cancer detection by utilizing deep learning convolutional neural network (CNN). Experiments are conducted using Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Compared to existing techniques, the used of CNN shows a better result and achieves 99.66%% in term of accuracy.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.319-322
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• 1997

This paper presents a CAD (Computer-Aided Diagnosis) system or detection of breast cancer, which is composed of personal computer, X-ray film scanner, high resolution display and application softwares. There are three major algorithms implemented in the application software. The irst algorithm is the adaptive enhancement of the digitized X-ray mammograms based on the first derivative and the local statistics. The second one is to detect the clustered microcalcifications by using the statistical texture analysis, and the third one is the classification of the clustered microcalcifications as malignant or benign by using the shape analysis. These algorithms were verified by real experiments.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.26 no.3
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• pp.211-219
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• 2022

Backgrounds/Aims: Historically, incidence and survival analysis and annual traits for primary liver cancer (LC) has not been investigated in a population-based study in Korea. The purpose of the current study is to determine incidence, survival rate of patients with primary LC in Korea. Methods: We conducted a retrospective cohort study using Korea Central Cancer Registry based on the Korea National Cancer Incidence Database. Statistical analysis including crude rate and age-standadized rate (ASR) of incidence and mortality was performed for LC patients registered with C22 code in International Classification of Diseases, tenth revision from 1999 to 2019. Subgroup analysis was performed for hepatocellular carcinoma (HCC, C22.0) and intrahepatic cholangiocarcinoma (IHCC, C22.1). Results: The crude incidence rate of HCC (21.0 to 22.8 per 100,000) and IHCC (2.3 to 5.6 per 100,000) increased in the observed period from 1999 to 2019. The ASR decreased in HCC (20.7 to 11.9 per 100,000) but remained unchanged in IHCC (2.4 to 2.7 per 100,000). The proportion of HCC patients diagnosed in early stages (localized or regional Surveillance, Epidemiology, and End Results or SEER stage) increased significantly over time. As expected, 5-yeat survival rate of HCC was greatly improved, reaching 42.4% in the period between 2013 and 2019. This trait was more prominent in localized SEER stage. On the other hand, the proportion of IHCC patients diagnosed in localized stage remained unchanged (22.9% between 2013 and 2019), although ASR and 5-year survival rate showed minor improvements. Conclusions: A great improvement in survival rate was observed in patients with newly diagnosed HCCs. It was estimated to be due to an increase in early detection rate. On the contrary, detection rate of an early IHCC was stagnant with a minor improvement in prognosis.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.6
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• pp.965-971
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• 2011

As the liver cancer in our country cancerous occurrence frequency to be the gastric cancer in the common cancer, If the case which will be discovered in early rising the treatment record was considered seriously about under the early detection. The system which it sees with the system for the early detection of the liver cancer reacts the blood of the control group other than the patient who is confirmed as the liver cancer and the liver cancer to the biochip and aptamer protein biochip profiles mechanical studying leads and it is a system which it classifies. 1149 each other it reacted blood samples of the control group other than the liver cancer patient who is composed of the total 85 samples and the liver cancer which is composed of 310 samples to the biochip which is composed with different oligo from the present paper and it was a data which it makes acquire worker the neural network it led and it analyzes the classification efficiency of the result 95.38 ~ 97.95% which it was visible.

• Proceedings of the PSK Conference
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• 2002.10a
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• pp.291.3-292
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• 2002

Cancer is multifaceted disease that presents many challenges to clinicians and cancer researchers searching for more effective ways to combat its often devastating effects. Among the central challenges of this disease, are the identification of markers for improved diagnosis and classification of tumors, and the definition of targets for more effective therapeutic measures. The objective of this study is to identify potential biomarkers for the early detection of gastric cancer in serum. (omitted)