AI tools in medical image analysis: efficacy of ANN for oestrogen receptor status assessment in immunohistochemical staining of breast cancer2013

– Special issue of International Journal of Pattern Recognition and Artificial Intelligence
– Expands upon papers from FLAIRS conference, covers various AI techniques and applications


This work studies the ability of artificial intelligence as a modern and an unconventional technique to automatically classify breast cancer cells and demonstrates the efficacy of the artificial neural network by recording an average rate of sensitivity, specificity and specificity.

– The study evaluates the efficacy of artificial neural network (ANN) in classifying breast cancer cells.
– ANN is compared with fuzzy c-means (FCM) and genetic algorithm (GA) techniques.

– AI and computational technology are revolutionizing interior design graphics and modeling.
– These technologies offer benefits such as design iterations, material visualization, and time optimization.

– Artificial neural network (ANN) is an effective technique for classifying breast cancer cells.
– Fuzzy c-means (FCM) and genetic algorithm (GA) were also compared.

– The paper explores healthcare staff perceptions on the benefits and challenges of using AI predictive tools in clinical decision-making.
– The study identifies opportunities for the application of AI predictive tools in clinical practice.

The paper discusses the use of artificial intelligence tools, specifically fuzzy c-means, artificial neural network, and genetic algorithm, for automatically classifying breast cancer cells based on oestrogen receptor status.

– Artificial neural network (ANN) can be used to automatically classify breast cancer cells.
– ANN shows efficacy in assessing oestrogen receptor status in breast cancer.

– Digital technology and tools are being used in the design industry.
– Artificial Intelligence (AI) is one of the latest computational technologies being utilized.

– The paper reviews existing AI technology and its potential applications in manufacturing systems.
– The paper discusses tools and techniques of AI relevant to the manufacturing environment.

– The paper discusses the application of stored programs in Artificial Intelligence.
– It focuses on production systems and their role in rule-based expert systems.