Oral Cancer Detection using Image Processing- Matlab

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Oral Cancer detection using Image Processing- Matlab

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Description

ABSTRACT

Tumor occurs in salivary glands, tonsils and also in neck, head, face and oral cavity. There are various diagnosis methods to find the oral cancer such as biopsy method in which a small sample of tissues being removed from a part of body and tested in microscope. And some  screening methods. But the drawback is we cannot actually clearly detect the tumor of cancer cells as well as we couldn’t classify how much cells are affected by cancer so in this paper we are going to detect and classify the affected cancerous cell in the oral region by digital Image processing techniques feature extraction enables clear visualization of cancer effected areas. Here we use firefly algorithm to detect the cancer tumor in the MRI image. And Back propagation neural network to classify the cancer cells accurately the project is carried out using mat lab program

INTRODUCTION

Tumor occurs in salivary glands, tonsils and also in neck, head, face and oral cavity people dies around 1,30000 in a year  with this cancer  According to American cancer society men face twice the risk of developing oral cancer as women, men who are over age 40 face greater risk, over 25% of all oral cancers occurs in people who do not smoke who drink alcohol occasionally 75% of oral cancer are due to use of tobacco and excessive alcohol consumption on other factors such as poor oral hygiene poor nutrition and by chronic infection caused by bacteria and virus.  Oral cancer occurs in mouth and throat Oral cancer is fairly common and Very curable if found and treated at an early stage. Oral cancer is also known as mouth cancer. This arises in any of tissues in mouth. There are several types of oral cancers, but around 90% are squamous cell carcinoma originating in the tissues that line the mouth and lips. Oral or mouth cancer most commonly involves on the floor of the mouth cheeks, gums, lips, or palate roof of the mouth. Most oral cancers look very similar under the microscope and are called squamous cell carcinoma The Signs and symptoms are such as on tongue and lips usually painless at initial, Burning sensation will occur if tumor gets advanced, Additional Problems are such as swallowing difficulty, mouth sores etc.

EXISTING SYSTEM

Oral cancer detection using tumor based marker using watershed algorithm. The techniques using in this paper is Orthopantomogram. And the watershed algorithm is proposed to preserve the edge details as well as prominent ones to identify tumors in dental radiographs. Marker Controlled Watershed segmentation is used to segment tumors because watershed on images leads to over segmentation even though it is preprocessed.

PROPOSED SYSTEM

Proposed methodology include enhancement of images we used salt and pepper noise, segmentation of cells are used for thresholding but that we can find the accurate results and find very easily of tumor by partition , features extraction, and finally the classification with the Back propagation neural networks.

BLOCK DIAGRAM

ORAL CANCER DETECTION USING MATLAB

ADVANTAGES

  • High accuracy
  • High precision value
  • Easy to find in the early stages

APPLICATIONS

  • Bio medical Imaging
  • Medical Test of testability
  • Laboratory test

SOFTWARE REQUIREMENTS

  • MATLAB 7.15 and above versions

REFERENCES

[1] Bolesina, N., Femopase, F. L., de Blanc, S. A. L., Morelatto, R. A., & Olmos, M. A. (2012). Oral squamous cell carcinoma clinical aspects. In Oral Cancer. InTech.

[2] Jung, W., Zhang, J., Chung, J., Wilder-Smith, P., Brenner, M., Nelson, J. S., & Chen, Z. (2005). Advances in oral cancer detection using optical coherence tomography. IEEE Journal of Selected Topics in Quantum Electronics, 11(4), 811-817

[3] Jiang, C. F., C. Y. Wang, and C. P. Chiang. “Oral cancer detection in fluorescent image by color image fusion.” Engineering in Medicine and Biology Society,. IEMBS’04. 26th Annual International Conference of the IEEE. Vol. 1. IEEE.

[4] Scully, Crispian, et al. “Oral cancer: current and future diagnostic techniques.” Am J Dent 21.4 (2008): 199-209

[5] Sharma, Neha, and Hari Om. “Extracting Significant patterns for oral cancer detection using apriori algorithm.” Intelligent Information Management 2014 (2014).

[6] K. Anuradha and K. Sankanarayanan, “Oral Cancer Detection Using Improved Segmentation Algorithm”, International Journal of Advance Research in Computer Science and Software Engineering, vol.5, issue 1, January, 2015.

[7] K. Anuradha* Dr. K. Sankaranarayanan “Oral Cancer Detection Using Improved Segmentation Algorithm “International Journal of Advanced Research in Computer Science and Software Engineering.

[8] Sharma, Neha, and Hari Om. “Extracting Significant patterns for oral cancer detection using apriori algorithm.” Intelligent Information Management 2014 (2014).

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