Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

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Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

In the analysis of medical images, one of the challenging tasks is the recognition of brain tumours via medical resonance images (MRIs) The diagnosis process is still tedious due to its complexity and considerable variety in tissues of tumor perception Therefore, the necessities of tumor identification techniques are improving nowadays for medical applications In the past decades, different approaches in the segmentation of various precisions and complexity degree have been accomplished, which depends on the simplicity and the benchmark of the technique An overview of this analysis is to give out the summary of the semi-automatic techniques for brain tumor segmentation and classification utilizing MRI An enormous amount of MRI based image data is accomplished using deep learning approaches There are several works, dealing on the conventional approaches for MRI-based segmentation of brain tumor Alternatively, in this review, we revealed the latest trends in the methods of deep learning Initially, we explain the several threads in MRI pre-processing, including registration of image, rectification of bias field, and non-brain tissue dismissal And terminally, the present state evaluation of algorithm is offered and forecasting the growths to systematise the MRI-based brain tumor into a regular cyclic routine in the clinical field are focussed

A comprehensive review on brain tumor segmentation and classification of MRI images

Salvaging tumor from T1-weighted CE-MR images using automatic segmentation techniques

Bias correction of intensity inhomogeneous images hybridized with superpixel segmentation

Bias field is a smooth intensity variation, which emanate during the process of image procurement Bias removal is an essential prerequisite while incorporating computer assisted diagnosis Several bias correction algorithms are proposed till date This paper scrutinizes the prominent bias correction algorithms, LEMS, MICO, BCFCM and N3 These investigations carried over the substantial amount of T1 and T2 weighted brain MR images with different bias spectrum from Brain Web website Algorithms efficiency are analyzed in spectral wise, slice wise, and type wise Based upon the performance indicators coefficient of variation and coefficient of joint variation, the algorithms are assessed and ranked The result concludes that which algorithm exterminates the bias field, presents in brain MR images in an efficient manner

Performance analysis of bias correction techniques in brain MR images

Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics.

/pdf/comparative-study-on-cancer-images-using-watershed-3e4odfmqut.pdf

Comparative Study on Cancer Images using Watershed Transformation

Oropharyngeal Cancer (OPC) is a type of oral cancer. Death rate for this Cancer is higher than the other types of oral cancer. Oropharyngeal Cancer Affects Oropharynx, Tonsils, Back Wall of The Tongue and Vallecula. In this work, various filtering methods are employed to remove the speckle noise from the CT image. CT images are widely used in detection of oropharyngeal cancer. Comparison of Anisotropic Diffusion Filter, Gaussian Filter and Adaptive Median Filter are used to remove the noise from CT image. The metrics used for analysis are PSNR, MSE, FOM, PR, SSIM. Its identified that Anisotropic Diffusion Filter provide best result.

/pdf/denoising-the-ct-images-for-oropharyngeal-cancer-using-bj9z2ywuw1.pdf

Denoising the CT Images for Oropharyngeal Cancer using Filtering Techniques

Rheumatic heart disease has a substantial impact on morbidity and mortality for both men and women in developing countries. It is a complication of autoimmune phenomenon known as acute rheumatic fever in response to group A streptococcus bacteria. It often causes damage to valves and lacks its functionality. Initial manifestations of prolapsed valves are evident in echocardiography in the form of valve bulging, commissural fusion and restricted leaflet motion. However, these echocardiogram images are inevitably degraded by multiplicative noise known as speckle noise at the time of image acquisition and transmission. Hence despeckling is of vital importance for enhancing ultrasound image quality. In this paper, a comparative analysis of well established despeckling filters compiled for speckle suppression and feature enhancement has been performed. In addition to this, the performance of these filters is validated using different quantitative metrics. The experimental result shows that feature enhanced Fast Non-Local Means (FNLM) filter outperforms other state-of-art filtering techniques in the aspect of higher PSNR value and preserves structural similarity in terms of SSIM value near to unity. In conclusion, the preprocessing intends to suppress speckle noise and enhancing perceived visual quality that aids for further development of virtual cardiac model.

Evaluation of Speckle Noise Reduction and Feature Enhancement in Prolapsed Mitral Valve Leaflet Echocardiography

Video files are the important source for the big data. Storing, managing and processing the video files are becoming more challenging day by day. Securing and retrieving the video files from the cloud storage has become one of the hot research topic as the vulnerability remains high. Usually, the confidential video files such as CCTV monitoring, OTT videos are transferred to the cloud storage using an insecure communication network. To protect the video file, existing technique uses a symmetric or single key encryption, which uses only one key to perform encryption and decryption. Symmetric key encryption reduces the work load key management and computation time. However, symmetric algorithms are vulnerable to various attacks as they use only single key to perform encryption and decryption. Likewise, existing video file encryption are inefficient to retrieve a particular block of data. In this research work, we create a hybrid model, which integrates the symmetric and asymmetric key encryption to tighten the security. Likewise, to retrieve a particular block of video file from the cloud storage, a bloom filter based index structure is maintained by the cloud service provider. The experimental result shows that the proposed model reduces the computation time and improves the security of the multimedia files stored in the cloud.

M. Mohamed Sathik

Papers

Performance analysis of bias correction techniques in brain MR images

Comparative Study on Cancer Images using Watershed Transformation

Denoising the CT Images for Oropharyngeal Cancer using Filtering Techniques

Evaluation of Speckle Noise Reduction and Feature Enhancement in Prolapsed Mitral Valve Leaflet Echocardiography

Secure and efficient retrieval of video file using bloom filter and hybrid encryption algorithms