Breast Tissue Classification via Interval Type 2 Fuzzy Logic Based Rough Set

BIRADS is a Breast Imaging, Reporting and Data System. A tool to standardize mammogram reports and minimizes ambiguity during mammogram image evaluation. Classification of BIRADS is one of the most challenging tasks to radiologist. An apt treatment can be administered to the patient by the oncologist upon acquiring sufficient information at BIRADS stage. This study aspired to build a model, which classifies BIRADS using mammograms images and reports. Through the implementation of type-2 fuzzy logic as classifier, an automatically generated rules will be applied to the model. Comparison of accuracy, specificity and sensitivity of the modal will be performed vis-à-vis rules given by the experts. The study encompasses a number of steps beginning with collection of the data from Radiology Department of National University of Malaysia Medical Center. The data was initially processed to remove noise and gaps. Then, an algorithm developed by selecting type-2 fuzzy logic using Mamdani model. Three types of membership functions were employed in the study. Among the rules that used by the model were obtained from experts as well as generated automatically by the system using rough set theory. Finally, the model was tested and trained to get the best result. The study shows that triangular membership function based on rough set rules obtains 89% whereas expert driven rules gains about 78% of accuracy rates. The sensitivity using expert rules is 98.24% whereas rough set rules obtained 93.94%. Specificity for using expert rules and rough set rules are 73.33%, 84.34% consecutively. Conclusion: Based on statistical analysis, the model which employed rules generated automatically by rough set theory fared better in comparison to the model using rules given by the experts.

Multilevel Thresholding of Brain Tumor MRI Images: Patch-Levy Bees Algorithm versus Harmony Search Algorithm

Image segmentation of brain magnetic resonance imaging (MRI) plays a crucial role among radiologists in terms of diagnosing brain disease. Parts of the brain such as white matter, gray matter and cerebrospinal fluids (CFS), have to be clearly determined by the radiologist during the process of brain abnormalities detection. Manual segmentation is grueling and may be prone to error, which can in turn affect the result of the diagnosis. Nature-inspired metaheuristic algorithms such as Harmony Search (HS), which was successfully applied in multilevel thresholding for brain tumor segmentation instead of the Patch-Levy Bees algorithm (PLBA). Even though the PLBA is one powerful multilevel thresholding, it has not been applied to brain tumor segmentation. This paper focuses on a comparative study of the PLBA and HS for brain tumor segmentation. The test dataset consisting of nine images was collected from the Tuanku Muhriz UKM Hospital (HCTM). As for the result, it shows that the PLBA has significantly outperformed HS. The performance of both algorithms is evaluated in terms of solution quality and stability

Machine Learning Methods for Breast Cancer Diagnostic

This chapter discusses radio-pathological correlation with recent imaging advances such as machine learning (ML) with the use of technical methods such as mammography and histopathology. Although criteria for diagnostic categories for radiology and pathology are well established, manual detection and grading, respectively, are tedious and subjective processes and thus suffer from inter-observer and intra-observer variations. Two most popular techniques that use ML, computer aided detection (CADe) and computer aided diagnosis (CADx), are presented. CADe is a rejection model based on SVM algorithm which is used to reduce the False Positive (FP) of the output of the Chan-Vese segmentation algorithm that was initialized by the marker controller watershed (MCWS) algorithm. CADx method applies the ensemble framework, consisting of four-base SVM (RBF) classifiers, where each base classifier is a specialist and is trained to use the selected features of a particular tissue component. In general, both proposed methods offer alternative decision-making ability and are able to assist the medical expert in giving second opinion on more precise nodule detection. Hence, it reduces FP rate that causes over segmentation and improves the performance for detection and diagnosis of the breast cancer and is able to create a platform that integrates diagnostic reporting system

Ischemic Stroke Lesion Segmentation Using Mutation Model and Generative Adversarial Network

Ischemic stroke lesion segmentation using different types of images, such as Computed Tomography Perfusion (CTP), is important for medical and Artificial intelligence fields. These images are potential resources to enhance machine learning and deep learning models. However, collecting these types of images is a considerable challenge. Therefore, new augmentation techniques are required to handle the lack of collected images presenting Ischemic strokes. In this paper, the proposed model of mutation model using a distance map is integrated into the generative adversarial network (GAN) to generate a synthetic dataset. The Euclidean distance is used to compute the average distance of each pixel with its neighbor in the right and bottom directions. Then a threshold is used to select the adjacent locations with similar intensities for the mutation process. Furthermore, semi-supervised GAN is enhanced and transformed into supervised GAN, where the segmentation and discriminator are shared the same convolution neural network to reduce the computation process. The mutation and GAN models are trained as an end-to-end model. The results show that the mutation model enhances the dice coefficient of the proposed GAN model by 2.54%. Furthermore, it slightly enhances the recall of the proposed GAN model compared to other GAN models