A new method based for diagnosis of breast cancer cells from microscopic images: DWEE--JHT

Building a Personalized Cancer Treatment System

This paper reports the process by which a personalized cancer treatment system was built, following a user-centered approach. We give some background on personalized cancer treatment, the particular tumor chemosensitivity assay supported by the system, as well as some quality and legal issues related to such health systems. We describe how Contextual Design was applied when building the system. Contextual design is a user-centered design technique involving seven steps. We also provide some details about the system implementation. Finally, we explain how the Think-Aloud protocol and Heuristic Evaluation methods were used to evaluate the system and report its results. A qualitative assessment from the users perspective is also provided. Results from the heuristic evaluation indicate that only one of ten heuristics was missing from the system, while five were partially covered and four were fully covered.

Diagnosis of cervical cancer cell taken from scanning electron and atomic force microscope images of the same patients using discrete wavelet entropy energy and Jensen Shannon, Hellinger, Triangle Measure classifier

The aim of this article is to provide early detection of cervical cancer by using both Atomic Force Microscope (AFM) and Scanning Electron Microscope (SEM) images of same patient. When the studies in the literature are examined, it is seen that the AFM and SEM images of the same patient are not used together for early diagnosis of cervical cancer. AFM and SEM images can be limited when using only one of them for the early detection of cervical cancer. Therefore, multi-modality solutions which give more accuracy results than single solutions have been realized in this paper. Optimum feature space has been obtained by Discrete Wavelet Entropy Energy (DWEE) applying to the 3×180 AFM and SEM images. Then, optimum features of these images are classified with Jensen Shannon, Hellinger, and Triangle Measure (JHT) Classifier for early diagnosis of cervical cancer. However, between classifiers which are Jensen Shannon, Hellinger, and triangle distance have been validated the measures via relationships. Afterwards, accuracy diagnosis of normal, benign, and malign cervical cancer cell was found by combining mean success rates of Jensen Shannon, Hellinger, and Triangle Measure which are connected with each other. Averages of accuracy diagnosis for AFM and SEM images by averaging the results obtained from these 3 classifiers are found as 98.29% and 97.10%, respectively. It has been observed that AFM images for early diagnosis of cervical cancer have higher performance than SEM images. Also in this article, surface roughness of malign AFM images in the result of the analysis made for the AFM images, according to the normal and benign AFM images is observed as larger, If the volume of particles has found as smaller.