Breast Cancer Risk Assessment by a Hybrid Interval Type-2 Fuzzy Cognitive Map Method

This paper proposes a new method for accessing the breast cancer risk called Hybrid High-order Interval Type-2 Fuzzy Cognitive Map (H-HIT2 FCM). In a simple Fuzzy Cognitive Map (FCM), the weights between nodes and activation functions are constant in each iteration. As an extension in the high order FCM, each node has a different transformation function to make it more flexible. However, using FCM or high order FCM can not make a favorable response in uncertain situations. Applying type-2 Fuzzy Cognitive Map to obtain the weights of FCM, the resulted method will have much better responses in such uncertain situations. An H-HIT2 FCM is proposed in this work, assessing breast cancer risk in three modes of optimistic, realistic, and pessimistic. The proposed method has three levels. In the first level, the patient's profile, family history, and the inherited factors are tested by high order FCM. In the second level, by examining the mass characteristics obtained from the mammograms, the disease risk is achieved by high-order interval type-2 FCM in three modes of optimistic, realistic, and pessimistic. The exact position of the tumor is obtained in the third level. Finally, a Support Vector Machine predicts an overall breast cancer risk. Moreover, compared to the existing methods, the accuracy of the results is desirable. The three-mode assessment will help the patients and their physician run the best treatment. The proposed method is successfully tested on a real radiology dataset, and the corresponding results are reported

Bone age estimation by deep learning in X-Ray medical images

Patient skeletal age estimation using a skeletal bone age assessment method is a time consuming and very boring process. Today, in order to overcome these deficiencies, computerized techniques are used to replace hand-held techniques in the medical industry, to the extent that this results in better evaluation. The purpose of this research is to minimize the problems of the division of existing systems with deep learning algorithms and the high accuracy of diagnosis. The evaluation of skeletal bone age is the most clinical application for the study of endocrinology, genetic disorders and growth in young people. This assessment is usually performed using the radiologic analysis of the left wrist using the GP (Greulich-Pyle) technique or the TW (Tanner-Whitehouse) technique. Both techniques have many disadvantages, including a lack of human deductions from observations as well as being time-consuming

Future of the Renal Biopsy: Time to Change the Conventional Modality Using Nanotechnology

At the present time, imaging guided renal biopsy is used to provide diagnoses in most types of primary and secondary renal diseases. It has been claimed that renal biopsy can provide a link between diagnosis of renal disease and its pathological conditions. However, sometimes there is a considerable mismatch between patient renal outcome and pathological findings in renal biopsy. This is the time to address some new diagnostic methods to resolve the insufficiency of conventional percutaneous guided renal biopsy. Nanotechnology is still in its infancy in renal imaging; however, it seems that it is the next step in renal biopsy, providing solutions to the limitations of conventional modalities