Integration of handcrafted and deep neural features for Melanoma classification
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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Brac University
2022
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| Online Access: | http://hdl.handle.net/10361/16905 |
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10361-169052022-06-06T21:01:28Z Integration of handcrafted and deep neural features for Melanoma classification Rahman, Mohammad Saminoor Hossain, Md. Jubayer Islam, Siful Kabir, Md. Nafiul Sujon, Md. Kamrul Hasan Alam, Md. Ashraful Reza, Md. Tanzim Department of Computer Science and Engineering, Brac University Skin cancer DNN Handcrafted feature Melanoma Ensemble Image segmentation Confusion Matrix Neural networks (Computer science) Signal processing -- Digital techniques -- Computer programs. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 44-46). Deep neural networks (DNNs) are widely utilized to automate medical image in- terpretation in many forms of cancer diagnosis and to support medical specialists with fast data processing. Although man-made characteristics have been used to diagnose since the 1990s, DNN is fairly new in this eld and has shown extremely promising results. The fundamental goal of this study is to detect melanoma cancer in its early stages by obtaining a remarkable outcome with greater accuracy. Our purpose is to address the problem of an increase in skin cancer patients throughout the world, as well as an exponential increase in the danger of mortality from not commencing the diagnosis at an early stage, as a result of late detection. We propose that the research works on handcrafted features and merges the result with deep learning approaches with the initial help with a huge dataset of raw images. The DNN model used in this research has multiple layers with various e ective lter- ing processes called batch normalization and dropout also with added layers named atten and dense. In this process, images are classi ed to predict melanoma cancer at an early stage with Mean Shift, SIFT, and Gabor separately then the output was ensembled with later added Raw images results to give better accuracy. With an early integration model for separate featured databases and with a late and full integration model for ensemble with various results from the early integrated model we got our results. As a result, this neural network has provided an accuracy of 90% in early models and in late and full integration 86% and 84% respectfully, which is higher than other conventional approaches. Mohammad Saminoor Rahman Md. Jubayer Hossain Siful Islam Md. Nafiul Kabir Md.Kamrul Hasan Sujon B. Computer Science 2022-06-06T05:04:19Z 2022-06-06T05:04:19Z 2021 2021-09 Thesis ID 17201136 ID 17301177 ID 16201050 ID 17101256 ID 16201070 http://hdl.handle.net/10361/16905 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 46 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Skin cancer DNN Handcrafted feature Melanoma Ensemble Image segmentation Confusion Matrix Neural networks (Computer science) Signal processing -- Digital techniques -- Computer programs. |
| spellingShingle |
Skin cancer DNN Handcrafted feature Melanoma Ensemble Image segmentation Confusion Matrix Neural networks (Computer science) Signal processing -- Digital techniques -- Computer programs. Rahman, Mohammad Saminoor Hossain, Md. Jubayer Islam, Siful Kabir, Md. Nafiul Sujon, Md. Kamrul Hasan Integration of handcrafted and deep neural features for Melanoma classification |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Rahman, Mohammad Saminoor Hossain, Md. Jubayer Islam, Siful Kabir, Md. Nafiul Sujon, Md. Kamrul Hasan |
| format |
Thesis |
| author |
Rahman, Mohammad Saminoor Hossain, Md. Jubayer Islam, Siful Kabir, Md. Nafiul Sujon, Md. Kamrul Hasan |
| author_sort |
Rahman, Mohammad Saminoor |
| title |
Integration of handcrafted and deep neural features for Melanoma classification |
| title_short |
Integration of handcrafted and deep neural features for Melanoma classification |
| title_full |
Integration of handcrafted and deep neural features for Melanoma classification |
| title_fullStr |
Integration of handcrafted and deep neural features for Melanoma classification |
| title_full_unstemmed |
Integration of handcrafted and deep neural features for Melanoma classification |
| title_sort |
integration of handcrafted and deep neural features for melanoma classification |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/16905 |
| work_keys_str_mv |
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