© MAR 2018 | IRE Journals | Volume 1 Issue 9 | ISSN: 2456-8880 IRE 1700401 ICONIC RESEARCH AND ENGINEERING JOURNALS 233 Classifying Deviations in Medical Microscopic Images Using Evolutionary Analysis KAKANI SUSMITHA 1 , GUNTUPALLI SAI TEJASWI 2 , KATAKAM LAKSHMI KALA 3 , KAKARLA PRECY PRANUTHA 4 , DR.SUDHIR TIRUMALASETTY 5 1,2,3,4 Department of Computer Science and Engineering, Vasireddy Venkatadri Institute of Technology 5 Professor, Department of Computer Science and Engineering,Vasireddy Venkatadri Institute of Technology,Nambur,Guntur,AP,India Abstract- Most of the patient diagnosis revolves around in identifying abnormalities in their respective medical images. These images are of various types, likely Ultrasound, CT Scan, MRI and microscopic images like bio-chemical slides, micro-biological slides & pathological slides. Few abnormalities are fractures, bad cells in blood, tumors, fungal identification etc. Finding the abnormal portions in these images needs expertise by the physician; this apt identification promotes and guarantees healthy medication by the physician or surgeon to patient. In medical microscopic images normal portions and abnormal portions are mixed together. None of the abnormal portions are related to abnormal and normal portions of image i.e. deviations are scattered among normal portions of image. These deviations are not present in some portions for specific area in the images. None of these deviations are overlapped nor can be grouped together into a single portion physically in the image. Deviations can be isolated along with normal portions of images. Identifying such deviations partially comes under classification and clustering. This project identifies deviations in Medical Microscopic images. These deviations can be identified visually which reveals about the presence of deviation but to know the percentage of deviation in a sample image is imperative. In-order to achieve this all deviations must be connected. This project connects all deviations using evolutionary analysis, includes the mixture functionalities of classification and clustering. Also this project uses BFS, DFS and random tracking for connecting deviations in the image. Index Terms- Deviation, Medical Images, Sickle cells I. INTRODUCTION Medical Imaging is the technique and process of creating virtual representations of the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues [5]. Different types of medical images are Scanned Images and Microscopic Images. Scanned images include MRI scan, CT Scan, PET, X-Ray, Ultrasound. Types of Medical images Magnetic Resonance Imaging (MRI) MRI is a medical imaging Technology that uses radio waves and a magnetic field to create detailed images of organs and tissues.MRI is used to evaluate blood vessels, Abnormal tissues, Bones and Joints, Spinal injuries etc[3]. Computed Tomography (CT) Computed Tomography (CT), is a medical imaging method that combines multiple X-ray projections taken from different angles to produce a detailed cross-sectional images of areas inside the body. CT images allow doctors to get very precise, 3D views of certain parts of the body. CT is used to evaluate presence, size, location of tumors, Bone injuries, Organs in chest, abdomen etc [3]. Position Emission Tomography (PET) PET is a nuclear imaging technique that provides physicians with information about how tissues and organs are functioning. PET is used to evaluate Neurological diseases such as Alzheimer’s and Multiple Sclerosis, Cancer etc [3]. Ultrasound Diagnostic ultrasound, also known as Medical Sonography, uses high frequency sound waves to create images of parts inside the body. It is used to evaluate Pregnancy, Abnormalities of heart and blood vessels etc[3]. X-Ray X-rays use ionizing radiation to produce images of a person’s internal structure by sending X-ray beams