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Archive Issue – Vol.3, Issue.1 (Jan-Mar 2023)

Archive Issue – Vol.3, Issue.1 (January-March 2023)


A Reivew on Automatic Diabetic Retinopathy Disease Diagnosis from Fundus Imaging

Satish Kumar Kushwaha, Neelesh Jain, Shekhar Nigam

Research Paper | Journal Paper

Vol.3, Issue.1, pp.1-05, Mar-2023

Abstract References Citation Download

Abstract

There have been a lot of examples in recent years when diabetes has been a factor. The most prevalent illness affecting individuals is this one. A person who has had this illness for a long period may also develop diabetic retinopathy, which can cause partial or total blindness depending on the health of the retina or the extent of the tissue damage. There is no cure for diabetic retinopathy, and there is no medication that can restore eyesight or the retina. It can only be avoided by taking care of it and getting regular checkups from doctors. It prevents blood from flowing to the retina, which causes blood vessels to enlarge and exudates to start leaking, which can result in partial or total blindness. This paper's goal is to evaluate numerous studies that have been conducted on diabetic retinopathy. Through fundus imaging, diabetic retinopathy may be automatically identified, and several methods have been developed to achieve a higher degree of accuracy with a low error rate. The system compares edge detection methods, classifiers, and machine learning methods that have been applied to automatic diabetic retinopathy disease diagnosis.

Key-Words / Index Term: Automatic Diabetic Retinopathy Diagnosis, Fundus Imaging, Optic Disc, Optic Cup, CNN, Retinal Image, Hemorrhages.

References

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Citation

Satish Kumar Kushwaha, Neelesh Jain, Shekhar Nigam "A Reivew on Automatic Diabetic Retinopathy Disease Diagnosis from Fundus Imaging" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.1, pp.1-05, 2023.

A Review on Automatic Lung Lesion Detection from Imaging Techniques

Raj Khatri, Neelesh Jain, Prateek Singhal

Research Paper | Journal Paper

Vol.3, Issue.1, pp.06-10, Mar-2023

Abstract References Citation Download

Abstract

Lung cancer is a terrible condition that can be fatal to people. It is the type of cancer that is most frequently diagnosed worldwide and is potentially fatal. Treatment is a potential remedy that might save a person's life if it can be identified sooner. This condition may be identified using a variety of imaging modalities, and it can then be appropriately treated. But compared to other imaging modalities like X-ray, ultrasound, and many more, computed tomography, sometimes known as a CT scan, is a superior alternative for accurately identifying illness. It saves time for medical professionals and spares lives if a condition can be automatically diagnosed. A basic checkup may become quicker and easier to complete. This paper's goal is to examine several already-in-use technologies for the automated detection of lung cancer. Numerous studies have been conducted using machine learning or image processing methods including CNN, DNN, edge detection, and others. This essay's goal is to outline the shortcomings and shortcomings of numerous systems that exist but are not fully utilised.

Key-Words / Index Term: Lung Cancer Detection, Lesion Classification, Imaging Techniques, CNN, DNN, X-Ray, CT Scan.

References

  1. A.M. Gindi, T.A. Al Attiatalla, M.M. Sami, “A Comparative Study for Comparing Two Feature Extraction Methods and Two Classifiers in Classification of Earlystage Lung Cancer Diagnosis of chest x-ray images.” Journal of American Science, 10(6), 2014, pp. 13-22.
  2. K. Suzuki, M. Kusumoto, S.I. Watanabe, R. Tsuchiya, H. Asamura, “Radiologic classification of small adenocarcinoma of the lung: radiologic-pathologic correlation and its prognostic impact.” The Annals of Thoracic Surgery, 81(2), 2006, pp. 413-419.
  3. Su C.C., et al. "Impact of low-dose computed tomography screening for primary lung cancer on subsequent risk of brain metastasis." J Thorac Oncol, 2021. DOI: 10.1016/j.jtho.2021.05.010.
  4. P. B. Sangamithraa and S. Govindaraju, "Lung tumour detection and classification using EK-Mean clustering," 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 2201-2206, doi: 10.1109/WiSPNET.2016.7566533.
  5. Selin Uzelaltinbulat, Buse Ugur, "Lung tumor segmentation algorithm," Procedia Computer Science, 120, 2017, pp. 140-147.
  6. G. Niranjana and M. Ponnavaikko, "A Review on Image Processing Methods in Detecting Lung Cancer Using CT Images," 2017 International Conference on Technical Advancements in Computers and Communications (ICTACC), pp. 18-25, doi: 10.1109/ICTACC.2017.16.
  7. Q. Wu and W. Zhao, "Small-Cell Lung Cancer Detection Using a Supervised Machine Learning Algorithm," 2017 International Symposium on Computer Science and Intelligent Controls (ISCSIC), pp. 88-91, doi: 10.1109/ISCSIC.2017.22.
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  9. Pang, Shanchen; Zhang, Yaqin; Ding, Mao; Wang, Xun; Xie, Xianjin, "A Deep Model for Lung Cancer Type Identification by Densely Connected Convolutional Networks and Adaptive Boosting." IEEE Access, 8, 2020, pp. 4799–4805.
  10. Agarwal, Aman & Patni, Kritik & Devarajan, Rajeswari. (2021). "Lung Cancer Detection and Classification Based on Alexnet CNN." doi: 10.1109/ICCES51350.2021.9489033.
  11. Li, Zirong; Li, Lian. "A novel method for lung masses detection and location based on deep learning." 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 963–969.
  12. Shi, Hui; Peng, Zhenwei; Wan, Honglin, "Pulmonary Nodules Detection Based on CNN Multi-scale Feature Fusion." 2019 IEEE 11th International Conference on Advanced Infocomm Technology (ICAIT), pp. 86–90.
  13. M. Vas and A. Dessai, "Lung cancer detection system using lung CT image processing," 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), pp. 1-5, doi: 10.1109/ICCUBEA.2017.8463851.
  14. W. Abdul, "An Automatic Lung Cancer Detection and Classification (ALCDC) System Using Convolutional Neural Network," 2020 13th International Conference on Developments in eSystems Engineering (DeSE), pp. 443-446, doi: 10.1109/DeSE51703.2020.9450778.
  15. D. Sharma and G. Jindal, ‘‘Computer aided diagnosis system for detection of lung cancer in CT scan images,’’ Int. J. Comput. Elect. Eng., 3(5), 2011, pp. 714–718.
  16. Shakeel, P.M., Burhanuddin, M.A. & Desa, M.I. "Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier." Neural Comput & Applic, 2020. https://doi.org/10.1007/s00521-020-04842-6.
  17. R. K. Samala, H.-P. Chan, C. Richter, L. Hadjiiski, C. Zhou, and J. Wei, “Analysis of deep convolutional features for detection of lung nodules in computed tomography,” Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 109500Q, 2019.
  18. D. Kumar, A. Wong, and D. A. Clausi, “Lung nodule classification using deep features in CT images,” Conference on Computer and Robot Vision, 2015, pp. 133–138.
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Citation

Raj Khatri, Neelesh Jain, Prateek Singhal, "A Review on Automatic Lung Lesion Detection from Imaging Techniques" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.1, pp.06-10, 2023.

Automatic Liver Lesion Extraction using Roberts Cross Edge Detection

Rachita Dubey, Lakhan Singh Sisodiya

Research Paper | Journal Paper

Vol.3, Issue.1, pp.11-15, Mar-2023

Abstract References Citation Download

Abstract

Liver cancer is one of the severe diseases that leading causes death. Extracting liver lesion is a bit challenging task in the field of image processing. It can be extracted from CT scan images by analyzing the liver shape at all possible extents. Primary liver cancer may originate itself as Hepatocellular Carcinoma. This paper proposed an automatic liver lesion extraction using Roberts Cross Edge Detection for better recognition rate. The Roberts processed with 2x2 matrixes with gradient magnitude that resulted sharp edge detection that can be dilated later for missing edge filling. System pertain various preprocessing techniques for enhancing the liver image that helps to extract liver lesion with high precision rate. This system indeed contributes in the field of medical science for detecting liver cancer automatically without any human intervention.

Key-Words / Index Term: Liver Lesion, Hepatocellular Carcinoma, Liver Cancer, Dilation, Roberts Cross Edge Detection, CT Scan, Gradient Magnitude.

References

  1. A. Dutta and A. Dubey, "Detection of Liver Cancer using Image Processing Techniques," 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2019.
  2. B. L. Priya, D. Saraswathi and R. P. Lakshmi, "Liver Segmentation using Weighted Contrast based Chan-Vese Method," 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2019.
  3. F. P. Romero et al., "End-To-End Discriminative Deep Network For Liver Lesion Classification," 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 2019.
  4. M. Y. Jabarulla and H. Lee, "Evaluating the effect of various speckle reduction filters on ultrasound liver cancer images," 2018 International Conference on Electronics, Information, and Communication (ICEIC), Honolulu, HI, 2018.
  5. Hong Zhou and Cheng Yang, "Flexible Nano-Ag@paper Biosensor and its Application in Detection of Liver Cancer," 2018 IEEE 13th Annual International Conference on Nano/Micro Engineered and Molecular Systems (NEMS), 2018.
  6. Muhammad Waseem Khan, S. Tai and Y. Lo, "Using Deep Learning to Evaluate the Segmentation of Liver Cell from Biopsy Image," 2018 9th International Conference on Awareness Science and Technology (iCAST), Fukuoka, 2018.

Citation

Rachita Dubey, Lakhan Singh Sisodiya "Automatic Liver Lesion Extraction using Roberts Cross Edge Detection" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.1, pp.11-15, 2023.

Secure Authentication for Input Credentials using CAPTCHA as a Graphical Password (CaRP)

Utkarsh Dubey, Arun Pratap Singh

Research Paper | Journal Paper

Vol.3, Issue.1, pp.16-20, Mar-2023

Abstract References Citation Download

Abstract

One of the most popular methods used by web services to protect their system against attacks is CAPTCHA. In essence, CAPTCHA is a Turing test that determines whether a human or a robot is accessing the system. Today's CAPTCHAs come in a variety of forms, and each has a different level of authentication. The CAPTCHA performs a crucial function in preventing spam entries and unauthorized access to a website. To securely authenticate the user in the proposed system, CAPTCHA is utilized to input the correct pairs of credentials. Proposed system prohibits input through keyboard and enhances security by dragging letters through mouse. In this system, CAPTCHA shows distorted alphabets of different colors and some colored bubbles are placed below. User needs to drag desired letter on the same colored bubble. Here in the developed method, identical shades of color are considered as same and every reload shuffles the color of letters and bubbles. A machine is not able to identify the identical shades for dragging up to desired one. System confuses bots by shuffling alphabets along with its shading. This is a new era of securing authentication system from various attacks using CAPTCHA.

Key-Words / Index Term: CAPTCHA, Authentication, Graphical Password, Image processing, Game, Robot.

References

  1. PNG Image, "CAPTCHA Code PNG 1," Accessed: 06-Dec-2022. Available: https://pngimage.net/captcha-code-png-1/
  2. B. Zhu, J. Yan, G. Bao, M. Yang, and N. Xu, "Captcha as Graphical Passwords—A New Security Primitive Based on Hard AI Problems," IEEE Transactions on Information Forensics and Security, vol. 9, pp. 891–904, 2014. doi: 10.1109/TIFS.2014.2312547
  3. S. Nirosha and U. Sridhar, "Captcha As Graphical Passwords—A New Security Primitive Based on Hard AI Problems," International Journal of Innovative Technology and Research (IJITR), vol. 5, no. 4, pp. 7030–7035, Jun.–Jul. 2017.
  4. S. S. Banne and K. N. Shedge, "CARP: CAPTCHA as A Graphical Password Based Authentication Scheme," International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 1, Jan. 2016.
  5. K. S. Kumar, "Captcha as Graphical Passwords," International Journal of Computer Science and Information Technologies (IJCSIT), vol. 6, no. 3, pp. 1975–1985, 2015.
  6. P. Pipersaniya and J. S. Nair, "Advanced CAPTCHA as a Graphical Password for Better Secure Authentication," International Journal of Innovations in Engineering and Technology (IJIET), 2017.
  7. V. K. Kolekar and M. B. Vaidya, "Click and Session Based—Captcha as Graphical Password Authentication Schemes for Smart Phone and Web," IEEE, 2015.
  8. A. K. and R. I. K., "Captcha As Graphical Passwords—Enhanced With Video-Based Captcha For Secure Services," IEEE, 2015.
  9. P. J. Kulkarni and G. M. Malwatkar, "The Graphical Security System by Using CaRP," IEEE, 2015.
  10. D. Vinod and A. S., "Captcha As Graphical Password For High Security," Global Journal of Advanced Engineering Technologies, 2015.
  11. P. J. Charde and M. S. Khandare, "Review Paper on Improved Security Using Captcha as Graphical Password," International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), 2016.

Citation

Utkarsh Dubey, Arun Pratap Singh "Secure Authentication for Input Credentials using CAPTCHA as a Graphical Password (CaRP)" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.1, pp.16-20, 2023.