In mammogram, masses are primary indication of breast cancer; and it is necessary to classify them as malignant or benign. In this classification task, Computer Aided Diagnostic (CAD) system by using ensemble learning is able to assist radiologists to have better diagnosis of mammogram images. Ensemble learning consists of two steps, generating multiple base classifiers and then combining them together. However, combining all base classifier in the ensemble model increases the computational cost and time. Therefore, ensemble pruning is an important step in ensemble learning to select the ensemble's members. Due to huge subsets of combination in the ensemble, selecting the proper ensemble subset is desirable. The objective of this paper is to select the optimal ensemble subset by using Bee Algorithm (BA). A pool of different classifier models such as Support vector machine, k-nearest neighbour and linear discriminant analysis classifiers have been trained using different samples of training data and 12 groups of features. Then, by using this pool of classifier models, BA was used to exploit and explore random uniform combination subsets of the trained classifiers. As a result, the best subset will be selected as the optimal ensemble. The mammogram image dataset that was used to test the model has been collected from Hospital Kuala Lumpur (HKL) and consists of 263 benign and malignant masses. The proposed method gives 77 % of Area Under Curve(AUC), 83% of accuracy, 93% of specificity and 60% of sensitivity.
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.
The
employment of combustion catalysts is an effective
way to improve
ammonium perchlorate (AP) decomposition performance during the combustion
process of composite solid propellants. A classic half-sandwich iron
carbonyl complex was proposed as the leading structure for exploring
high-performance combustion catalysts, in which functionalized cyclobutadienes
(Cb) tune the thermal stability and catalytic activity. The thermolysis
of Fe2(CO)9 and substituted alkynes initiated
[2 + 2] cyclization of alkyne triple bonds and gave η4-cyclobutadiene iron(0) tricarbonyl complexes, CbR1,R2-Fe-CO (1–6), with decent yields.
A molecular structure analysis found that the conjugation of the aromatic
substituents aryl, ferrocenyl (Fc), and triazinyl (Tz) finely tuned
the coordination bonds around the Fe(0) center. The DSC/TG experiments
found a remarkable thermal stability of CbTz,R-Fe-CO (3–6) with characteristic thermolysis temperatures
(CTT) as high as 500 °C. The catalytic experiments demonstrated
that the CTT of Cb-Fe-CO (2-6) overlapped
with AP high-temperature decomposition (HTD). The thermal cross-coupling
of HTD of AP and CTT of CbTz,Fc-Fe-CO (6)
significantly augmented catalytic AP decomposition, resulting in a
maximum energy release as high as 2828 J/g.
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. To evaluate the proposed model, accuracy, specificity and sensitivity of the modal will be calculated and compared vis-à-vis rules given by the experts. The study encompasses a number of steps beginning with collection of the data from Radiology Department, Hospital of National University of Malaysia (UKM). 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 rules achieve 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.
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