Optimization of Convolutional Neural Network ensemble classifiers by Genetic Algorithms

Breast cancer exhibits a high mortality rate and it is the most invasive cancer in women. An analysis from histopathological images could predict this disease. In this way, computational image processing might support this task. In this work a proposal which employes deep learning convolutional neural networks is presented. Then, an ensemble of networks is considered in order to obtain an enhanced recognition performance of the system by the consensus of the networks of the ensemble. Finally, a genetic algorithm is also considered to choose the networks that belong to the ensemble. The proposal has been tested by carrying out several experiments with a set of benchmark images.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Additives for enhancing the drying properties of adhesives for corrugated boards

Adhesives play a fundamental role in many modern technologies, and adhesive failure can have catastrophic consequences. It is, therefore, valuable to understand the factors important for the production of a good durable adhesive bond. The additives are also used to enrich the properties. The objective of this paper is to increase the drying speed of the starch adhesive by adding suitable additives and thereby increasing the production speed of corrugated board manufacturing. The other functional additives that could be incorporated in minor amounts for better drying speed are studied and selected. Their properties such as drying speed, strength, viscosity and pH are tested. The results from the tests are compared and the best additive for fast drying is selected

Computer-aided diagnostic system for breast cancer detection based on optimized segmentation scheme and supervised algorithm

Breast cancer is a serious threat to the womankind and it leads the susceptible kinds of cancer for women. The mortality rates due to breast cancer increases every single year and the World Health Organization (WHO) aims to reduce the occurrence of breast cancer by at least 2.5% per year. The occurrence of breast cancer can be minimized only when periodical screening is carried out. Mammography is one of the effective screening procedure, which can effectively locate earlier signs of breast cancer. As an aid, this work aims to present a system for the breast cancer detection and classification. This work is segregated into four phases and all these phases aim to enhance the classification performance. The efficiency of the proposed work is evaluated against the state-of-the-art approaches and the proposed contribution to the medical science. The computer-aided diagnostic system (CADS) proves 98.2% accuracy, with minimal false positive and false negative rates in a reasonable period of time