The manual examination of histological images like computed tomography (CT) images by physicians is prone to subjectivity and limited intra and inter-surgeon reproducibility, due to its heavy reliance on human interpretation. As result of which, diagnosis of cancer especially in lungs becomes less accurate and unreliable. So, a computer-aided diagnosis (CAD) system, based on artificial intelligence that efficiently detects nodules of any shape and size, is used for diagnosis without human intervention. In this work, we have developed a two stage CAD system in which the first stage involves pre-processing applied for a better quality image to enable higher success rate on detection following which the cancerous nodule region is segmented. The second stage involves artificial neural network (ANN) architecture which is trained using a modified BFGS algorithm.The proposed system was trained, tested, and evaluated specifically on the problem of detecting lung cancer nodules found on CT images to give a positive detection. A significant comparative analysis was done between the proposed method and several existing CAD systems used for lung nodule diagnosis and the proposed method using training-based neural networks prove to provide accuracy of 96.7% and also better specificity; thus, the overall performance of the CAD scheme was improved substantially.
To prevent non-licensees from driving and therefore causing accidents, a new system is proposed. An important and very reliable human identification method is fingerprint identification. Fingerprint identification is one of the most popular and reliable personal biometric identification methods. The proposed system consists of a smart card capable of storing the fingerprint of particular person. While issuing the license, the specific person's fingerprint is to be stored in the card. Vehicles such as cars, bikes etc should have a card reader capable of reading the particular license. The same automobile should have the facility of fingerprint reader device. A person, who wishes to drive the vehicle, should insert the card (license) in the vehicle and then swipe his/her finger. If the finger print stored in the card and fingerprint swiped in the device matches, he/she can proceed for ignition, otherwise ignition will not work. Moreover, the seat belt detector verifies and then prompts the user to wear the seat belt before driving. This increases the security of vehicles and also ensures safe driving by preventing accidents.
Line jitter due to loss of horizontal synchronization from a noisy video source is a particular video artifact. Line jittering or random horizontal displacements of lines in video images occur when the synchronization signals are corrupted in video storage media, or by electromagnetic interference in wireless video transmission which results in a vertical rolling effect in the digitized video. It is however a curious problem which gives rise to some interesting algorithms. A non-linear algorithm based on detection and estimation technique for restoration of video sequences corrupted by jitter is proposed. The algorithm presented here relies on first applying median filter and then adaptive median filtering on the jittered images. The removal of these artifacts is achieved without destroying important image features like edges and details. The concept is extended for the video sequences. A fast block-matching algorithm based on variable shape search is used for the video sequences and finally temporal filtering is applied to this to obtain the restores video sequence. The proposed algorithm produces better results visually and also video quality indexes, 881M, P8NR are better compared to the different image sequences restoration algorithms.
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