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Archive Issue – Vol.3, Issue.4 (Oct-Dec 2023)

Archive Issue – Vol.3, Issue.4 (October-December 2023)


A Review on Automatic Skin Cancer Detection in the Field of Image Processing

Nitin Nagle, Naveen Jain, Ankur Taneja

Research Paper | Journal Paper

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

Abstract References Citation Download

Abstract

Skin cancer incidence is rising in several countries, particularly in India, making automatic skin cancer detection a critical and challenging task in medical image processing. Such detection systems typically involve two key steps: first, identifying skin anomalies, and second, classifying them as benign or malignant. Traditionally, skin cancer diagnosis has relied on invasive conventional techniques, although various commercial diagnostic tools and auxiliary methods are now available. This paper reviews the diverse approaches for skin cancer detection, encompassing stages like image preprocessing and classification. Preprocessing is essential for enhancing image quality by removing noise and irrelevant background elements. This comprehensive review critically examines existing literature in the field, highlighting recent advancements along with their respective advantages and limitations.

Key-Words / Index Term: Automatic Skin Cancer Detection, Support Vector Machine, Convolutional Neural Network, Machine Learning, Image Processing, Melanoma.

References

    • Mayo Clinic, Skin Cancer: Symptoms & Causes
    • The Local Choice, How to Lower Your Skin Cancer Risk
    • E. Jana, R. Subban, & S. Saraswathi, "Research on Skin Cancer Cell Detection Using Image Processing," 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, 2017, pp. 1-8, doi: 10.1109/ICCIC.2017.8524554.
    • F. K. Nezhadian & S. Rashidi, "Melanoma skin cancer detection using color and new texture features," 2017 Artificial Intelligence and Signal Processing Conference (AISP), Shiraz, 2017, pp. 1-5, doi: 10.1109/AISP.2017.8324108.
    • P. M. Amulya & T. V. Jayakumar, "A study on melanoma skin cancer detection techniques," 2017 International Conference on Intelligent Sustainable Systems (ICISS), Palladam, 2017, pp. 764-766, doi: 10.1109/ISS1.2017.8389278.
    • P. Bumrungkun, K. Chamnongthai, & W. Patchoo, "Detection skin cancer using SVM and snake model," 2018 International Workshop on Advanced Image Technology (IWAIT), Chiang Mai, 2018, pp. 1-4, doi: 10.1109/IWAIT.2018.8369708.
    • S. Mane & S. Shinde, "A Method for Melanoma Skin Cancer Detection Using Dermoscopy Images," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 2018, pp. 1-6, doi: 10.1109/ICCUBEA.2018.8697804.
    • Shalu & A. Kamboj, "A Color-Based Approach for Melanoma Skin Cancer Detection," 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India, 2018, pp. 508-513, doi: 10.1109/ICSCCC.2018.8703309.
    • N. B. Linsangan, J. J. Adtoon, & J. L. Torres, "Geometric Analysis of Skin Lesion for Skin Cancer Using Image Processing," 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines, 2018, pp. 1-5, doi: 10.1109/HNICEM.2018.8666296.
    • G. Mansutti, A. T. Mobashsher, & A. Abbosh, "Design of a Millimeter-Wave Near-Field Probe for Early-Stage Skin Cancer Detection," 2019 13th European Conference on Antennas and Propagation (EuCAP), Krakow, Poland, 2019, pp. 1-4.
    • M. Z. Hasan, S. Shoumik, & N. Zahan, "Integrated Use of Rough Sets and Artificial Neural Network for Skin Cancer Disease Classification," 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 2019, pp. 1-4, doi: 10.1109/IC4ME247184.2019.9036653.
    • G. Mansutti, A. T. Mobashsher, & A. M. Abbosh, "Millimeter-wave substrate integrated waveguide probe for near-field skin cancer detection," 2018 Australian Microwave Symposium (AMS), Brisbane, QLD, 2018, pp. 81-82, doi: 10.1109/AUSMS.2018.8346992.
    • H. Alquran et al., "The melanoma skin cancer detection and classification using support vector machine," 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Aqaba, 2017, pp. 1-5, doi: 10.1109/AEECT.2017.8257738.
    • Neha & A. Kaur, "Wearable antenna for skin cancer detection," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 197-201, doi: 10.1109/NGCT.2016.7877414.
    • K. Roy, S. S. Chaudhuri, S. Ghosh, S. K. Dutta, P. Chakraborty, & R. Sarkar, "Skin Disease detection based on different Segmentation Techniques," 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, India, 2019, pp. 1-5, doi: 10.1109/OPTRONIX.2019.8862403.
    • E. Vocaturo, D. Perna, & E. Zumpano, "Machine Learning Techniques for Automated Melanoma Detection," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 2019, pp. 2310-2317, doi: 10.1109/BIBM47256.2019.8983165.

Citation

Nitin Nagle, Naveen Jain, Ankur Taneja, "A Review on Privacy Preservation over Data Leakage in Cloud" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.4, pp.1-05, 2023.

A Comprehensive Review on Automatic Soil Classification in Image Processing

Abhishek Dubey, Shrikant Zade

Research Paper | Journal Paper

Vol.3, Issue.4, pp.06-12, Dec-2023

Abstract References Citation Download

Abstract

Soil classification is the segmentation approach based on its features such as textural, geographical, chemical and physical. Since the soil stores are endlessly fluctuated component in this world, it has not been found imaginable to make a general arrangement of soil classification for separating soils into different gatherings and subgroups based on their significant list of properties. Be that as it may, helpful frameworks in view of a couple of recorded properties that have been formulated. A portion of these frameworks are in such normal use by workers in different fields including soils that the architect should have essentially overall information on them. Simultaneously it is fundamental for remember that no framework can satisfactorily portray soil of all designing purposes. Soil classification is the detachment of soil into classes or gatherings each having comparative qualities and possibly comparable conduct. A classification for the end goal of designing should be founded principally on mechanical properties, for example penetrability, firmness, strength. The class to which a soil has a place can be utilized in its portrayal. The intention of this paper is to review various earlier implemented systems that classify the soil on the basis of textural properties. Many of the systems are based on machine learning approaches where they use deep neural networks. But the problem with the deep neural network is that if utilized network has been not used then weight of the network might be bulky that increase the size of the network and system get slower to execute and training and testing may take long time.

Key-Words / Index Term: Soil Classification, Soil Identification, Image Processing, Textural Data, Soil Analysis, Feature Extraction, Clustering.

References

    1. Duarte, Isabel M. R. and Rodrigues, Carlos M. G., Pinho, "Classification of Soils", Encyclopedia of Engineering Geology, 2018, Springer International Publishing, pp. 125-13, doi:10.1007/978-3-319-73568-9_52.
    2. Srivastava, Pallavi, Shukla, Aasheesh & Bansal, Atul, "A comprehensive review on soil classification using deep learning and computer vision techniques," Multimedia Tools and Applications, 80, 2021, doi: 10.1007/s11042-021-10544-5.
    3. H. K. Sharma & S. Kumar, "Soil Classification & Characterization Using Image Processing," 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), 2018, pp. 885-890, doi: 10.1109/ICCMC.2018.8488103.
    4. S. Shivhare & K. Cecil, "Automatic Soil Classification by using Gabor Wavelet & Support Vector Machine in Digital Image Processing," 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 1738-1743, doi: 10.1109/ICIRCA51532.2021.9544897.
    5. Vijay E V, Navya Ch, Abdul Shabana Begum, Rajaneesh D, Mahesh Babu B, "Soil Classification Using Modified Support Vector Machine," International Journal of Research in Advent Technology, Vol.8, No.9, September 2020.
    6. R. Pittman & B. Hu, "Improvement of Soil Texture Classification with LiDAR Data," IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 5018-5021, doi: 10.1109/IGARSS39084.2020.9324152.
    7. A. V. Deorankar & A. A. Rohankar, "An Analytical Approach for Soil and Land Classification System using Image Processing," 2020 5th International Conference on Communication and Electronics Systems (ICCES), 2020, pp. 1416-1420, doi: 10.1109/ICCES48766.2020.9137952.
    8. B. Bhattacharya & D.P. Solomatine, "Machine learning in soil classification," Neural Networks, Volume 19, Issue 2, 2006, pp. 186-195.
    9. P. A. Harlianto, T. B. Adji & N. A. Setiawan, "Comparison of machine learning algorithms for soil type classification," 2017 3rd International Conference on Science and Technology - Computer (ICST), 2017, pp. 7-10, doi: 10.1109/ICSTC.2017.8011843.
    10. Bhargavi, Peyakunta & Singaraju, Jyothi, "Soil Classification Using GATree," International Journal of Computer Science and Information Technology, 2, 2010, pp. 184-191, doi:10.5121/ijcsit.2010.2514.
    11. S. A. Z. Rahman, K. Chandra Mitra & S. M. Mohidul Islam, "Soil Classification Using Machine Learning Methods and Crop Suggestion Based on Soil Series," 2018 21st International Conference of Computer and Information Technology (ICCIT), 2018, pp. 1-4, doi: 10.1109/ICCITECHN.2018.8631943.
    12. M. van Rooyen, N. Luwes & E. Theron, "Automated soil classification and identification using machine vision," 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), 2017, pp. 249-252, doi: 10.1109/RoboMech.2017.8261156.
    13. K. Srunitha & S. Padmavathi, "Performance of SVM classifier for image based soil classification," 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016, pp. 411-415, doi: 10.1109/SCOPES.2016.7955863.
    14. Shravani V1, Uday Kiran S2, Yashaswini J S3, Priyanka D, "Soil Classification And Crop Suggestion Using Machine Learning," International Research Journal of Engineering and Technology (IRJET), Volume 07, Issue 06, June 2020.
    15. A. Bonini Neto, C. dos Santos Batista Bonini, B. Santos Bisi, A. Rodrigues dos Reis & L. F. Sommaggio Coletta, "Artificial Neural Network for Classification and Analysis of Degraded Soils," IEEE Latin America Transactions, vol. 15, no. 3, pp. 503-509, March 2017, doi: 10.1109/TLA.2017.7867601.
    16. Koresh, H. James Deva, "Analysis of Soil Nutrients based on Potential Productivity Tests with Balanced Minerals for Maize-Chickpea Crop," Journal of Electronics, 3(01), 2021, pp. 23-35.
    17. Joe, C. Vijesh & Jennifer S. Raj, "Location-based Orientation Context Dependent Recommender System for Users," Journal of Trends in Computer Science and Smart Technology (TCSST), 3(01), 2021, pp. 14-23.
    18. G. Huluka & R. Miller, "Particle size determination by hydrometer method," Southern Cooperative Series Bulletin 419, pp. 180-184, 2014.
    19. D. L. Rowell, "Soil science: Methods & applications," Routledge, 2014.
    20. P. R. Day, "Particle fractionation and particle-size analysis," Methods of Soil Analysis. Part 1, pp. 545-567, 1965.
    21. W. W. Rubey, "Settling velocity of gravel, sand, and silt particles," American Journal of Science, vol. 148, pp. 325-338, 1933.
    22. L. Beuselinck, "Grain-size analysis by laser diffractometry: comparison with the sieve-pipette method," Catena, pp. 193-208, 1998.
    23. P. K. Monye, P. R. Stott & E. Theron, "Assessment of reliability of the hydrometer by examination of sediment," in Proceedings of the 1st Southern African Geotechnical Conference, Sun City, South Africa, 5-6 May 2016.
    24. B. G. Batchelor, "Machine Vision Handbook," Springer, 2017.
    25. P. R. Stott & E. Theron, "Shortcomings in the estimation of clay fraction by the hydrometer," Journal of the South African Institution of Civil Engineering, vol. 58, pp. 14-24, 2016.
    26. V. Sudharsan & B. Yamuna, "Support Vector Machine based Decoding Algorithm for BCH Codes," Journal of Telecommunication and Information Technology, 2016.
    27. B. Bhattacharya & D.P. Solomatine, "An algorithm for clustering and classification of series data with constraint of contiguity," Proc. 3rd Int. Conf. on Hybrid and Intelligent Systems, Melbourne, Australia, 2003, pp. 489-498.
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Citation

Abhishek Dubey, Shrikant Zade, "A Comprehensive Review on Automatic Soil Classification in Image Processing" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.4, pp.06-12, 2023.

Palm Based Recognition System: A Review

Neha Dangi, Atul Barve

Research Paper | Journal Paper

Vol.3, Issue.4, pp.13-16, Dec-2023

Abstract References Citation Download

Abstract

There are certain biometric features through which a human can uniquely identified. Some of them are fingerprint, iris, face and palm print. Fingerprint based authentication is more popular as compare to others, but palm print is newly introduced feature through which a system can be developed that can works like fingerprint authentication system. A handprint, by virtue of covering more skin area, includes more identifying details, making false positives all but impossible and simultaneously making intentional falsification much more difficult. In other situations, such as criminal investigations, a full or partial palm print may sometimes be obtained when fingerprints are absent. A criminal might, for example, wear gloves to avoid leaving fingerprints but inadvertently leave a partial palm print when a glove slips during the commission of a crime. There are so many systems proposed till now which are based on palm print and there are so many techniques through which palm print features can be extracted. Some of them are Local Binary Pattern, Gabor filter, Weber’s Local Descriptor and many more. Palm print based authentication system can be implemented in both mode either by touch based or touch-less.

Key-Words / Index Term: Palm Print, Authentication, Crease, Touch, Touch-less.

References

    1. A. S. Parihar, A. Kumar, O. P. Verma, A. Gupta, P. Mukherjee, and D. Vatsa, “Point Based Features for Contact-less Palmprint Images,” IEEE Transactions, 2013.
    2. A. George, G. Karthick, and R. Harikumar, “An Efficient System for Palm Print Recognition using Ridges,” IEEE Transactions, 2014.
    3. I. Awate and B. A. Dixit, “Palm Print Based Person Identification,” IEEE Transactions, 2015.
    4. G. Jaswal, R. Nath, and A. Kaul, “Texture Based Palm Print Recognition using 2-D Gabor Filter and Sub Space Approaches,” IEEE Transactions, 2015.
    5. A. D. Aishwarya, G. M. Saranya, and R. K. Saranya, “Palm Print Recognition Using Liveness Detection Technique,” IEEE Transactions, 2016.
    6. S. Kaushik and R. Singh, “A New Hybrid Approach for Palm Print Recognition in PCA Based Palm Print Recognition System,” IEEE Transactions, 2016.
    7. S. Agarwal, P. K. Verma, and M. A. Khan, “An Optimized Palm Print Recognition Approach using Gabor Filter,” IEEE Transactions, 2017.
    8. S. Rajeev and S. Agaian, “3-D Palmprint Modeling for Biometric Verification,” IEEE Transactions, 2017.
    9. Academy of Hand Analysis, “Hand Printing and Palm Analysis,” Available: http://academyofhandanalysis.org/tag/hand-printing/page/3/ , Accessed: Sept. 2023.

Citation

Neha Dangi, Atul Barve, "Palm Based Recognition System: A Review" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.4, pp.13-16, 2023.

A Review on Image Steganography for Data Hiding

Sumit Kumar Sahgal

Research Paper | Journal Paper

Vol.3, Issue.4, pp.17-22, Sep-2023

Abstract References Citation Download

Abstract

The word Steganography has been achieved from two Greek words-'stegos' signifying 'to cover' and 'grayfia', signifying 'composing', subsequently meaning 'covered composition', or 'stowed away composition'. Steganography is a technique for concealing privileged information, by inserting it into a sound, video, image, or text record. It is one of the techniques utilized to shield mysterious or delicate information from pernicious assaults. Cryptography and steganography are the two strategies used to stow away or ensure privileged information. In any case, they vary in the regard that cryptography makes the information ambiguous, or conceals the importance of the information, while steganography conceals the presence of the information. In layman's terms, cryptography is like composing a letter in a mysterious language: individuals can understand it, however will not get what it implies. Nonetheless, the presence of a (most likely confidential) message would be clear to any individual who sees the letter, and assuming that somebody either knows or sorts out your mysterious language, then, at that point, your message can undoubtedly be perused.

Key-Words / Index Term: Image Steganography, Advanced Encryption Standard, Least Significant Bit.

References

    1. Mayo Clinic: Diabetic Retinopathy – Symptoms & Causes.
    2. CV Eye Medical Group: Diabetic Eye Care.
    3. Medgadget: Diabetic Retinopathy Market Report, 2019.
    4. Eye Physicians and Surgeons, “Diabetic Retinopathy,” Available: https://www.mteps.com/services/diabetic-retinopathy/, Accessed: 10 May 2021.
    5. Wikipedia: Color Mapping.
    6. Histogram Equalization – Towards Data Science.
    7. Thresholding – HIPR2.
    8. Wikipedia: MATLAB.
    9. University of Tartu: Image Processing eBook.
    10. GeeksforGeeks: Digital Image Processing Basics.
    11. K. K. Palavalasa and B. Sambaturu, “Automatic Diabetic Retinopathy Detection Using Digital Image Processing,” Proc. ICCSP, Chennai, 2018, pp. 72–76. doi: 10.1109/ICCSP.2018.8524234.
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    14. A. Osareh, B. Shadgar, and R. Markham, “A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 4, pp. 535–545, 2009.
    15. M. S. Solanki and A. Mukherjee, “Diabetic Retinopathy Detection Using Eye Image.”
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    22. R. Klein, B. E. Klein, S. E. Moss, M. D. Davis, and D. L. DeMets, “The Wisconsin Epidemiologic Study of Diabetic Retinopathy. II Prevalence and Risk of Diabetic Retinopathy When Age at Diagnosis is Less Than 30 Years,” Arch Ophthalmology, vol. 102, pp. 527–532, 1984.
    23. B. Harangi, I. Lazar, and A. Hajdu, “Automatic Exudate Detection Using Active Contour Model and Region Wise Classification,” Proc. IEEE EMBS, 2012, pp. 5951–5954.
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Citation

Sumit Kumar Sahgal, "A Review on Image Steganography for Data Hiding" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.4, pp.17-22, 2023.