Automatic detection of ovarian cancer based on improved DWT transformation - Rashid, Sabreena; Kaur, Rajdeep (International Journal of Engineering and Technology, 2018)
Ovarian cancer sub-kinds are distinct pathologic individual with dissimilar prognostic and therapeutic suggestions. Histo-typing by pathologists has good reproducibility; therefore, occasional cases are challenging and require immune histo chemistry and sub-specialty discussion. Motivated by the need for more accurate and reproducible diagnosis and to facilitate pathologistâ€™s work-flow, implement an automated system for ovarian cancer classification and identification. The main problem discussed for detecting procedure fields: (i) the cancer detection on ultra sound image is not easy to classify on the basis of clustering or segmentation. It can involve the False Acceptance Rate and False Rejection Rate higher at the interval of time recognition from the knowledge base.(ii)The working accuracy rate is 90 to 95 of Normal SVM existing systems. Our technique is implemented by detection of the cancer stage accordingly workflow. We implement images of cancer at two enlargement and extract features like a, color, text and shape data using digital image processing techniques. We analyze the machine-learning algorithm and spatial domain algorithm used to extract the features in four phases: LL, HL, LH and HH. Extract the features used to dimension reduction and a SVM classification to divide the 5 ovarian cancer stages. The research paper represents, the details of our implementation and its validate on (Govt. hospital) clinically derived database of high-resolution diagnosis images. The new system attained a linear classification accuracy 98% when classifying un-seen tissues. The method has been implemented using simulation tool using MATLAB 2016a. The Ovarian Stages were then tested for the accuracy using transformation software. Testing consequences defined an accuracy of 94%, Specificity 0.99 and Sensitivity value is 0.9978 for MRI Medical Images respectively.Â Â Â Â