Abstract
One of the leading cause of death is cancer, which is an irregular and unusual cell growth, which tends to escalate in an uninhibited way and in certain cases, metastasize. It is not any one particular disease but is an assembly of more than hundred dissimilar and distinctive diseases. Studies insinuates and evinced that cancer in esophagusis recognized and triaged as the sixth most common cause of death owing to any form of cancer while eighth most commonly materializing cancer across the world. Feasibly, indicating it to be one of the most deleterious diseases that has the potential and is likely of taking several lives in no time. It is indeed a growing health concern that is anticipated to amplify in incidence over a very short span of time. The scanty and limited improvements in the conventional techniques of treatment of this cancer have suggested seeking new and innovative approach and strategy of treatment. The main purpose is to make a Computer aided diagnosis system which can easily detect the cancerous portion. Here two video samples are taken for the work, one is of normal esophageal and another of cancerous. The videos are split into number of image samples, from them a few images are considered as training samples and rest of the images are taken as testing images. The proposed framework is followed by the application of Discrete Wavelet Transform for image transformation and Principal Component Analysis for the feature extraction and finally the comparison between the testing and training images are achieved using Logarithm Similarity Measure. The outcomes demonstrate an accuracy of more than 87%. The accuracy results might be high, if the database should have sufficient and accurate in respect of resolution of image samples. This result is high enough than some benchmark and well known frameworks. Outcome obtained prove the experiment to be highly efficient and requires a very less amount of time of operation thereby making it extremely useful in the diagnosis of esophageal cancer.
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Acknowledgements
The authors are highly obliged to the Department of Electrical Engineering, Techno International New Town (Formerly, Techno India College of India), Kolkata, India for their constant support and moral help. Though the work is not supported by any Foundation, the laboratory of the institute was helped to do the work smoothly. We thank our colleagues from [Techno International New Town (Formerly, Techno India College of India)] who provided insight and expertise that greatly assisted the research, although they may not agree with all of the interpretations of this paper.
We thank [Mr. Arabindo Chandra, Mrs. Satabdi Chatterjee, Ms. Swarnali Jhampati, Ms. Ayindrila Roy of Electrical Engineering, Techno International New Town, Mr. Sandip Joardar, Assistant Manager, Mr. Anustup Chatterjee, Assistant Professor, Department of Mechanical Engineering, Techno International New Town, Koklata. Haldia Petrochemicals Pvt. Ltd., Mr. Susovan Bhaduri, Electronics and Communication Department, Jadavpur University] for assistance with [theoretical concept], and [Dr. Milan Basu, Head of the Department of Electrical Engineering, Techno International New Town] for comments that greatly improved the manuscript.
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Chatterjee, S., Biswas, M., Maji, D., Ghosh, B.K., Mandal, R.K. (2020). Logarithm Similarity Measure Based Automatic Esophageal Cancer Detection Using Discrete Wavelet Transform. In: Balas, V., Kumar, R., Srivastava, R. (eds) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-030-32644-9_33
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