Qualitative Metrics on Breast Cancer Diagnosis with Neuro Fuzzy Inference Systems

Image Processing represents the main research area within engineering and computing specialization. It is promptly rising technologies today, and its applications found in various aspects of biomedical fields specifically in cancer disease. Breast cancer is taken into account the fatal one in all cancer types in step with recent statistics everywhere the globe. It's the foremost common cancer in women and also the second reason of cancer death between females. In this paper, we recommend implementing a fast segmentation algorithm employing a watershed transformation. This extends the partitioning of the dividing waterline by allowing the mixing of advance information about image objects and therefore the traditional watershed algorithm. Before the watershed transformation can begin, the algorithm needs a way of representing the test image in terms of the amount of change around any particular pixel. We apply the Sobel operator to each pixel in the grayscale representation of the original image. The tumors detected are circular or semicircular in shapes according to the shape, and the brightness of the tumor will be darker as we moved far away from its center. The complement for this prior information can be taken as a local minimum that required beginning the watershed algorithm. So each tumor image can be represented as a lake with minimum value represented by the center in the complement tumor image. After employing the method, the detection of tumor percentage becomes more reliable. Such result indicates that the new technique has improved the performance of our computer aided diagnosis system for mammographic breast cancer detection effectively. The algorithm was implemented in MATLAB and tested under Windows system. The method was tested over several images from MIAS (Mammogram Image Analysis Society, UK), that provides a standardized classification for mammographic studies. In this paper, we recommend implementing a quick segmentation algorithm employing a watershed transformation. This extends the partitioning of the dividing waterline by allowing the mixing of advance information about image objects and therefore the traditional watershed algorithm.

Section: Discussionsupporting

confidence: 58%

Section: Watershed Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Mammogram Breast Cancer Detection using Fast Watershed Segmentation

Shahin¹

2020

“…Breast Cancer Diagnosis with Neuro Fuzzy Inference Systems (BCD-NFIS) contributes to decreased use of data-sets features by using the Fuzzy networks. This ends, in effect, with an improved accuracy of 98.24% compared to previous methodologies [43].…”

Section: Literature Reviewmentioning

confidence: 75%

Breast Cancer Predictive Analytics Using Supervised Machine Learning Techniques

Akinsola¹

2019

Breast cancer unarguably has been the very prominent disease amongst women as well as the next most dangerous after lung cancer. Early diagnosis and prevention is of paramount importance. Several methods such as micro-array analysis and network analysis have been proffered but they are somewhat expensive and time consuming. There is a need to develop an automated system based on Machine learning techniques to detect breast cancer early. Benign and Malignant tumors were classified using Logistic Regression (LRO), Bayes Network (BNK), Multilayer Perceptron (MLP), Sequential Minimal Optimization (SMO), J48, Naive Bayes (NBS) and Instance Based Learner (IBK) algorithms, which were implemented in Waikato Environment for Knowledge Analysis (WEKA). The breast cancer database for this study was collected from the University of Wisconsin Hospitals, published on California College, Irvive (UCI) website. The five most critical performance metrics when selecting an algorithm in model building in the health related domain are Area Under the ROC Curve (AUC), Receiver Operating Characteristics Curve (ROC), Mean Absolute Error (MAE), Accuracy and Kappa Statistic. In relation to the results of Accuracy, Precision and Kappa Statistic which were evaluated and compared, BNK has best predictive accuracy of 97.14%, followed by SMO with 96.71%, then LOR with 96.57%. On the other hand, LOR has the highest AUC of 99.3%, followed by BNK with 99.2%, then SMO with 96.5%. Beyond accuracy, AUC should be keenly considered in algorithm selection and model building. Therefore, Logistic Regression should be chosen as the best classifier instead of Bayes network for breast cancer optimal prediction.

“…The healthcare organization has enormous volume of data and it is poorly utilized for obtaining the knowledge [1]. The healthcare systems now a day are playing vast role with the recent tools and technologies for disease predictions [2]. The healthcare organizations have more amounts of patient's data and not utilized properly for making the decisions [3].…”

Section: Introductionmentioning

confidence: 99%

Analyzes of Mouth Cancer Using Max-Min Composition in Soft Computing

Jothikumar¹

2019

The intention of making this paper is for the reason that uncertainty in medical reasoning has turned out to be in massive augmentation that it is complicated to be handled. So by means of lending a hand, that is where of the above mentioned health-open-handed system to agree on the concerned problematic factor of mouth cancer, totally using Max-Min composition is found out. Here these lethal types of the cancer, when measured up along with other different and dissimilar cancers that this age group , band that is the generation from this century individuals have and even had been and still being pretentious , in more better expanded way, they have an outcome on all human and beings over the whole wide universe. From the acquired in sequence info, that is from the clinician experts reporting that Smoking, rises of Tobacco products and Alcohol using up in excess or the shortage and avoid doing from too much fuss of drinking are the reason cause for Mouth cancer. Here these causes mentioned according to the clinicians' info are unsure, doubtful and situation to find the effects of mouth cancer are complex, so for this Max-min composition is worn to analyze this issue.