Alex Pandian Tamil Movie Download Single Part
Boleslao Drinker
Alex Pandian is an unapologetic masala mixture of mindless action, double-meaning comedy, titillating songs, superficial romance, shallow sentiments, cardboard villains and weak characterizations held together by tweet-size story. That pretty much summarizes Alex Pandian.
Alex Pandian is about a convict who kidnaps the CM daughter for money, falls in love during the process, later changes his mind to save her from an army of enemies who have a sinister plan against the people of Tamil Nadu. Alex Pandian is directed by Suraaj who used the same formula as his previous movies such as 'Padikathavan', 'Maapillai', "Marudamalai", etc. Studio Green has produced this movie with the single objective of pushing Karthi to "Mass" hero status. Did they succeed?
Karthi has literally got an on-your-face introduction scene aimed at pleasing the masses. The opening fight scenes are jaw-dropping and Karthi appears to have pulled off some difficult stunt sequences. Even though we don't understand the motive for the fight, the opening fight is picturized and choreographed well. Karthi's comedy timing with Santhanam is the highlight of the movie. Karthi continues to show notable improvement in his dance moves and also appears more confident in making animated expressions in songs. Karthi's dialogue modulation is good as always, despite showing some monotony.
No doubt, first half is a complete laugh riot. Full credits to Santhanam and Karthi's unbeatable comedy timing. Santhanam continues to maintain his top form in this movie. The story in the first half single threads on Santhanam trying to protect his 3 flirtatious sisters from Karthi's antics. Karthi and Santhanam's one-liners give a thunderous start to the movie. Santhanam gets full marks and key reason for keeping us engaged during the first half. However, it is still a surprise how the censor gave a clean "U" certificate allowing double-entendres such as the lewd dialogues in the carrom board playing scenes with Santhanam's sisters.
Anushka hardly appears during the first half. With hero's help, she escapes from the enemies in the opening fight sequence and once again during the interval fight sequence. She goes missing-in-action during rest of the first half. Anushka, who appears to be a bit bulky, doesn't have much to do other than being carried around by Karthi and for doing standard "DSP" dance-routines. Other than Anushka, there are 3 sisters of Santhanam repeatedly flirting with Karthi in song and comedy scenes throughout the first half.
Comedy department has done their job well. The movie has 3 comedians (Santhanam, Lollu-saba Manohar and Manobala). Karthi's comedy with Mano Bala in the second-half largely falls flat and is of poor humour. The stark contrasts between the first and second half comedy scenes clearly highlight the limitations of Karthi when performing without Santhanam.
There are close to 8 full-time/part-time villains (Suman, Milind Soman, Prathap, Santhanabarathy, Saravanan, director Raj kapoor, familiar looking villain acting as Samiyar and a new-face villain acting as Saravanan's brother). None of them make any impact, whatsoever.
Music Director Devi Sri Prasad (DSP) continues to deliver kuthu numbers along the lines of "Singam" songs. DSP's BGM focuses more on knuckle-cracking sounds as Karthi fights the villains. Camera work by Saravanan during the first half and opening stunt sequence are fantastic. Stunt and dance choreographers have worked hard to portray Karthi as "mass" hero.
Director Suraaj repeats his action-comedy genre once again in Alex Pandian. The first half is entirely dominated by Karthi and Santhanam's comedy scenes, even though the scenes are totally unrelated to the story line. The villain blocks are abruptly forced in between the comedy sequence without any logic or coherence. Right when the director starts focusing on moving the story forward in the second-half, the movie starts faltering big time and slips to abysmal depth by the time the end credit (finally) rolls.
Suraaj, Karthi and Studio Green have made this formula movie under-estimating the intelligence of Tamil audience. Karthi is keen on pursuing the path that our Tamil "mass" heroes have shunned of-late. Encouraged by the success of Siruthai, Karthi seems to be fully convinced that this is his path to massdom in Tamil and Telugu film industries. From business perspective, Studio Green may recover the money during the long Pongal weekend. But, the real question is whether such movies actually do any good for Karthi's career in the long run. People's verdict will validate whether Karthi is on the right track in his choice of movies.
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Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images. In this architecture, the encoder and decoder are designed using an inverted residual block and swish activation function. It also employs a feature pyramid attention network between the encoder and decoder to extract exact dense features for pixel classification. The proposed architecture was compared to the existing UNet architecture, and the proposed methodology yielded significant results. The proposed model was comprehensively trained and validated using the LIDC-IDRI dataset available in the public domain. The experimental results revealed that the Lung_PAYNet delivered remarkable segmentation with a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%.
Lung nodule segmentation involves extraction of a nodule region with a boundary from the lung parenchyma. In addition, it is a challenging task for radiologists to find nodule regions when they are attached to lung vessels, lung walls, and other internal structures.
Lung nodules can be classified based on their position in the lungs. Lung nodules that are not attached to any nearby structures are well-circumscribed. Juxta-pleural nodules are attached to the lung parenchyma and juxta-vascular nodules are affixed to the blood vessels. Figure 1 illustrates the various types of lung nodules obtained from the LIDC-IDRI dataset4. Depending on the malignancy factor, there may be benign (non-cancerous) or malignant (cancerous) lung nodules. It is necessary to segment the nodule carefully because it is critical to determine the malignancy factors.
Although many image-processing-based segmentation techniques5,6,7 have been applied to lung nodule segmentation, there is no generalized segmentation framework for segmenting various lung nodules using a single technique. Techniques suitable for segmenting well-circumscribed nodules may not be suitable for segmenting juxta-pleural or juxta-vascular nodules. This study focuses on end-to-end data-driven deep learning approaches to segment different types of lung nodules.
Scientists have attempted to construct an exceedingly accurate, effective, and automatic lung nodule segmentation system that can help doctors to segment lung nodules. These attempts have been categorized into two major categories: image processing-based models and deep learning-based models.
Image processing models include morphology operations, region growing algorithms, and energy optimization techniques are the most frequently used methods. In morphology-based methods, researchers employed morphological opening operation and connected component selection methods to remove the vessels attached with nodules. However, separating lung nodules with extensive contact areas with other lung structures is challenging with the fixed size morphological template. As a result, more complicated morphological processes combining shape assumptions have been introduced.
Kuhnigk et al.8 found that blood vessel radii decreased as they evolved towards the perimeter of the lungs. Moreover, they recommended the use of rolling ball filters in combination with a rule-based analysis of juxta-pleural nodules. The selection of the morphological template size is a significant challenge for morphological approaches because it is difficult to identify an appropriate template for the morphology of diverse nodule sizes. The performance measure used in this work is median error and it was 3.1%.
Dehmeshki et al.9 developed a shape-based hypothesis to extract nodules from the lung wall. Here the segmentation method depended on sphericity oriented contrast region growing on the fuzzy connectivity map and the segmentation accuracy is 84%. Kubota et al.10 created a probability map to represent the probability of every pixel belonging to a nodule based on the local gray level. Diciotti et al.11 defined a semi-automatic technique based on region-growing for the 3D-segmentation of lung nodules in spiral CT images.
Zhou et al.12 introduced a fully automatic lung segmentation method for juxta-pleural nodules. A nonlinear anisotropic diffusion filtering method was employed to reduce image noise. The thoracic region was extracted using thresholding, 2D hole filling, and the largest connected-component search method. The lung parenchyma was separated using a fuzzy c-means algorithm, region-growing algorithm, and dynamic programming approach. An algorithm based on an adaptive curvature threshold has been proposed to include juxta-pleural nodules in the lung parenchyma. The average FP error was 1.89%, the average FN error was 2.39%, and the average volumetric overlap fraction was 95.81%.
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