Contralateral asymmetry for breast cancer detection : A CADx approach

Early detection is fundamental for the effective treatment of breast cancer and the screening mammography is the most common tool used by the medical community to detect early breast cancer development. Screening mammograms include images of both breasts using two standard views, and the contralateral asymmetry per view is a key feature in detecting breast cancer. we propose a methodology to incorporate said asymmetry information into a computer-aided diagnosis system that can accurately discern between healthy subjects and subjects at risk of having breast cancer. Furthermore, we generate features that measure not only a view-wise asymmetry, but a subject-wise one. Briefly, the methodology co-registers the left and right mammograms, extracts image characteristics, fuses them into subjectwise features, and classifies subjects. In this study, 152 subjects from two independent databases, one with analog- and one with digital mammograms, were used to validate the methodology. Areas under the receiver operating characteristic curve of 0.738 and 0.767, and diagnostic odds ratios of 23.10 and 9.00 were achieved, respectively. In addition, the proposed method has the potential to rank subjects by their probability of having breas

Fermatean Fuzzy Programming with New Score Function: A New Methodology to Multi-Objective Transportation Problems

The aim of this work is to establish a new methodology to tackle the multi-objective transportation problems [MOTP] in a Fermatean fuzzy environment that can deal with all the parameters that possess a conflicting nature. In our research work, we developed a new score function in the context of a fermatean nature for converting fuzzy data into crisp data with the help of the Fermatean fuzzy technique. Then, we introduced an algorithm-based methodology, i.e., the Fermatean Fuzzy Programming approach to tackle transportation problems with multi-objectives. The main purpose of this research work is to give an alternate fuzzy programming approach to handle the MOTP. To justify the potential and validity of our work, numerical computations have been carried out using our proposed methodology

A Fuzzy Optimization Technique for Multi-Objective Aspirational Level Fractional Transportation Problem

In this research work, a soft computing optimization operating approach is developed for a multi-objective aspirational level fractional transportation problem. In the proposed technique, a mathematical model is formulated for the multi-objective aspirational level fractional transportation problem (MOFTP) based on the highest value of one and all objectives of the model. We also used the symmetry concept over our model to identify the best optimum solution based on symmetrical data. We constructed the membership grades for the set of fetched parameters having symmetry. In this work, we also used the concept of ranking function in our mathematical model to obtain the optimum solution of the fuzzy multi-objective fractional transportation. In this proposed algorithm, the aspiration levels are also associated with the objective function of MOFTP. We are also proposing a new approach for the optimization of fractional problems in which the objectives are being optimized by using the numerator function and denominator function simultaneously. Further, a methodology is also developed to find the average cost for each fractional objective of the model. After that, we will find the ranking function for each parameter by using the defuzzification method. By this methodology, we will be able to convert the MOFTP into a bi-objective transportation problem. The provided technique is elaborated with the help of numerical computations to prove the beauty and power of the proposed technique