MEDICAL IMAGE ENHANCEMENT USING DEEP LEARNING, NEUTRAL NETWORKS, AUTO ENCODERS
ABSTARCT :
One common interest in radiography is producing radiographs with as low as possible radiation exposures to patients. In clinical practices, radiation exposure factors are preset for optimal image qualities to avoid underexposures which will lead to repeating examinations hence increasing radiation exposures to patients. Underexposed radiographs mainly suffer from Poisson noises due to inadequate photons reaching the detector. Radiographs are often degraded by scatter radiations and the severity of image quality degradations depends on the amount of scatters reaching the detectors. In this work, a convolutional neural network (CNN) algorithm was used to predict scatters and reduce Poisson noises. Monte Carlo simulation images and an adult abdomen radiograph were used to evaluate this CNN algorithm. The radiograph was underexposed by 60% radiation exposures. The simulation images were produced with one-thousandth of a typical clinical exposure. The results show that Poisson noises are successfully reduced, and image contrast and details are improved. After the underexposed radiograph which is not useful for making a confident diagnosis was processed using the CNN algorithm, the contrast and details in the radiograph were greatly improved and are adequate for making a diagnosis, therefore a 60% radiation dose reduction was achieved. This work shows that radiograph qualities can be improved by reducing scatters and Poisson noises. A potential application of this CNN algorithm is for patient radiation dose reductions by reducing current preset optimal radiation exposures and then using this algorithm to enhance the image contrast and details by reducing both scatters and Poisson noises.
EXISTING SYSTEM :
? Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems.
? Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly.
? Deep learning (DL) has emerged as the go-to methodology to drastically enhance the performance of existing machine learning techniques and to solve previously intractable problems.
DISADVANTAGE :
? Machine learning started as a field in computer science to endow algorithms to solve problems without being explicitly programmed.
? It typically learns representations from training data, which are generalized in separate test data.
? Sometimes the features are difficult to define for a given problem. Researchers need to choose from different combinations of features, algorithms, and degrees of complexity to sufficiently solve a given problem, and many studies depend on trial-and-error to find the right combination.
? A major challenge in particular is choosing the right features to correctly model a given problem.
PROPOSED SYSTEM :
• Multilayer perceptron (MLP) was proposed to improve the simple perceptron model by adding hidden layers and developing learning techniques, such as back-propagation.Two studies proposed algorithms to generate a high-quality image (e.g., high dose CT) from a low quality image (e.g., low dose CT) using CNN, which reduced noise and provided more structural information than the low-quality images.
• An automatic algorithm would perform the first round of labeling and then the human experts can either accept or modify the results of the first round. Recently, one study proposed a DL method to automatically retrieve images from a large database that matched human set criterion
ADVANTAGE :
? The advantages of DL far outweigh its shortcomings, and thus, it will be an essential tool for diagnosis and prognosis in the era of precision medicine.
? They show excellent performance when applied to training data but typically suffer losses in performances when applied to independent validation data.
? This is partly due to the overfitting of the training data. Performance of machine learning techniques must be evaluated with both training and independent validation data.
? A slight modification of these parameters might lead to drastically different models with varying performances.
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