Lung Nodule Detection Using Image Processing Matlab Project With Source Code || IEEE Based Projects

ABSTRACT

        Lung nodule prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung nodule diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of nodule cells in the chest. Lung nodule diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung nodule in patients. Hence, there is need for a system that is capable for detecting lung nodule automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung nodule detection. The aim of this research is to design a lung nodule detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted and classified using support vector machine. This method is implemented to detection of lung nodule of lung samples.

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Prof. Roshan P. Helonde

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Fruit Disease Detection Using CNN Convolutional Neural Network | Python Project With Source Code || IEEE Based Projects

ABSTRACT

            Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. Fruit diseases can cause significant losses in yield and quality appeared in harvesting. For example, soybean rust (a fungal disease in soybeans) has caused a significant economic loss and just by removing 20% of the infection, the farmers may benefit with an approximately 11 million-dollar profit (Roberts et al., 2006). Some fruit diseases also infect other areas of the tree causing diseases of twigs, leaves and branches. An early detection of fruit diseases can aid in decreasing such losses and can stop further spread of diseases. A lot of work has been done to automate the visual inspection of the fruits by machine vision with respect to size and color. However, detection of defects in the fruits using images is still problematic due to the natural variability of skin color in different types of fruits, high variance of defect types, and presence of stem/calyx. To know what control factors to consider next year to overcome similar losses, it is of great significance to analyze what is being observed. In this project, Fruit Disease Detection done Using CNN Convolutional Neural Network in Python. The image processing based proposed approach is composed this project. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases.

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Prof. Roshan P. Helonde

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Matlab Source Code for Audio Steganography - Data Hiding In Audio Using Matlab Project with Source Code

 ABSTRACT

          Information security is one of the most important factors to be considered when secret information has to be communicated between two parties. Cryptography and steganography are the two techniques used for this purpose. Cryptography scrambles the information, but it reveals the existence of the information. Steganography hides the actual existence of the information so that anyone else other than the sender and the recipient cannot recognize the transmission. In steganography the secret information to be communicated is hidden in some other carrier in such a way that the secret information is invisible. In this project an audio steganography technique is proposed to hide audio signal in image in the transform domain using wavelet transform. The audio signal in any format wav is encrypted and carried by the image without revealing the existence to anybody. When the secret information is hidden in the carrier the result is the stegno signal. In this work, the results show good quality stegno signal and the stegno signal is analyzed for different attacks. It is found that the technique is robust and it can withstand the attacks. The quality of the stegno signal is measured by Peak Signal to Noise Ratio (PSNR), Mean Square Error. The quality of extracted secret audio signal is measured by Signal to Noise Ratio (SNR). The results show good values for these metrics.

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Prof. Roshan P. Helonde

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Python Source Code On Steganography for Hiding Message in Image || Steganography Using Python Project Source Code

 ABSTRACT

           Steganography is the process of hiding a secret message within a larger one in such a way that someone can not know the presence or contents of the hidden message. The purpose of Steganography is to maintain secret communication between two parties. Unlike cryptography, which conceals the contents of a secret message, steganography conceals the very fact that a message is communicated. In this project secret message is embedding in cover image while sending to receiver at extraction part secret message is extracted from cover image which is called as message hiding in image. Although steganography differs from cryptography, there are many analogies between the two, and some authors classify steganography as a form of cryptography since hidden communication is a type of secret message.

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Prof. Roshan P. Helonde

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Python Source Code for Image Compression | Image Compression Using Python Project With Source Code

 ABSTRACT

            The lossless compression is that allows the original data to be perfectly reconstructed from the compressed data. Lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that probable. The main objective of image compression is to decrease the redundancy of the image data which helps in increasing the capacity of storage and efficient transmission. Image compression aids in decreasing the size in bytes of a digital image without degrading the quality of the image to an undesirable level. Image compression plays an important role in computer storage and transmission. The purpose of data compression is that we can reduce the size of data to save storage and reduce time for transmission. Image compression is a result of applying data compression to the digital image.

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Prof. Roshan P. Helonde

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Matlab Code for Liver Cancer Detection Using Image Processing Full Project Source Code

 ABSTRACT

        Liver cancer is one of the most severe types ofcancerous diseases which is responsible for the death of many patients. Liver cancer images have more noises which is difficult to diagnose the level of the tumor. It is a challenging task to automatically identify the tumor from images because of several anatomical changes in different patients. The cancer is difficult to find because of the presence of objects with same intensity level. In this proposed system, fully automated machine learning is used to detect the liver tumor from input image. Region growing technique is used to segment the region of interest. The textural feature are extracted from Gray level co-occurrence matrix (GLCM) of the segmented image. Extracted textural features are given as input to the designed SVM classifier system. Performance analysis of SVM classification of liver cancer image is studied. This will be useful for physician in better automatic diagnosis of liver caner from input images.

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Mr. Roshan P. Helonde

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Matlab Code for Blood Cancer Detection using Image Processing

ABSTRACT

             Leukemia Blood cancer is the most prevalent and it is very much dangerous among all type of cancers. Early detection of blood cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose blood cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the technician. To obviate these problems, image processing techniques is use in this study as promising modalities for detection of Leukemia blood cancer. The accuracy rate of the diagnosis of blood cancer by using image processing will be yield a slightly higher rate of accuracy then other traditional methods and will reduce the effort and time. We first discuss the preliminary of cell biology required to proceed to implement our proposed method. This project presents a new automated approach for blood Cancer detection and analysis from a given photograph of patient’s cancer affected blood sample. The proposed method is using image improvement, image segmentation for segmenting the different cells of blood, edge detection for detecting the boundary, size, and shape of the cells and finally Clustering for final decision of blood cancer based on the number of different cells.

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Mr. Roshan P. Helonde

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Matlab Code for Steganography using AES Algorithm Information Hiding Using AES Algorithm

ABSTRACT

            In today’s world, confidential information is growing due to various areas of works. Internet is the main area of transmission of digital data, so security must be more considered. Two common ways of providing security is cryptography and steganography. Employing a hybrid of cryptography and steganography enhances the security of data. This project employs LSB (Least significant Bit) as the steganography algorithm and AES algorithms as cryptographic algorithms to encrypt a message that should be hidden in a cover image. The results are represented in the form of execution time, PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error). The experimental results reveal that the algorithms achieve appropriate quality of stego image. They can be used as cryptographic algorithms to encrypt a message before applying steganography algorithms.

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Mr. Roshan P. Helonde

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WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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