Application of deep convolutional neural network in multiclass skin cancer classification using custom CNN architecture
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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| 语言: | English |
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Brac University
2023
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| 在线阅读: | http://hdl.handle.net/10361/21934 |
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10361-219342023-12-07T21:02:24Z Application of deep convolutional neural network in multiclass skin cancer classification using custom CNN architecture Shafique, Nadia Shaheen, Kaynat Bint Sikder, Zarjis Husain Dey, Utsho Swacha, Sharforaz Rahman Ashraf, Faisal Bin Department of Computer Science and Engineering, Brac University Skin cancer CNN model BKL Convolution layer Accuracy AUC MEL BCC AKIEC Deep learning Dermoscopic data Convolutional Neural Network NV DF ROC curve F1 score VASC Machine learning Cognitive learning theory Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 56-59). Skin diseases represent a significant global health concern, and prompt and pre- cise diagnosis is necessary for efficient treatment. Convolutional Neural Networks (CNNs), in particular, have shown tremendous promise in the diagnosis of skin dis- eases due to their capacity for processing and learning from complex patterns in visual data. Employing 28x28 RGB images taken from the HAM10000 dataset, the purpose of this work is to develop and assess a customized CNN model created exclusively to aid in the classification of different skin conditions. This method al- lows the model to efficiently learn the distinctive characteristics of each type. Our model is evaluated using a number of metrics, such as accuracy, precision, recall, and F1-score. We have also compared our results to well-known pre-trained models like ResNet50 and EfficientNetB0/B2. In comparison to existing pre-trained mod- els, our own model performs better due to its increased test accuracy, reduced test loss, and computational parameters. Additionally, it has fewer trainable parame- ters as well as a shorter training time per epoch, which makes it appropriate for deployment in situations with constrained computational resources. In conclusion, Our model promises to improve diagnostic accuracy, perhaps enabling earlier and more effective methods for diseases of the skin because of its higher performance and computational advantages. Nadia Shafique Kaynat Bint Shaheen Zarjis Husain Sikder Utsho Dey Sharforaz Rahman Swacha B.Sc. in Computer Science and Engineering 2023-12-07T06:10:44Z 2023-12-07T06:10:44Z 2023 2023-05 Thesis ID 18201138 ID 19101408 ID 19101630 ID 19301042 ID 23141040 http://hdl.handle.net/10361/21934 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. 59 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Skin cancer CNN model BKL Convolution layer Accuracy AUC MEL BCC AKIEC Deep learning Dermoscopic data Convolutional Neural Network NV DF ROC curve F1 score VASC Machine learning Cognitive learning theory Neural networks (Computer science) |
| spellingShingle |
Skin cancer CNN model BKL Convolution layer Accuracy AUC MEL BCC AKIEC Deep learning Dermoscopic data Convolutional Neural Network NV DF ROC curve F1 score VASC Machine learning Cognitive learning theory Neural networks (Computer science) Shafique, Nadia Shaheen, Kaynat Bint Sikder, Zarjis Husain Dey, Utsho Swacha, Sharforaz Rahman Application of deep convolutional neural network in multiclass skin cancer classification using custom CNN architecture |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Ashraf, Faisal Bin |
| author_facet |
Ashraf, Faisal Bin Shafique, Nadia Shaheen, Kaynat Bint Sikder, Zarjis Husain Dey, Utsho Swacha, Sharforaz Rahman |
| format |
Thesis |
| author |
Shafique, Nadia Shaheen, Kaynat Bint Sikder, Zarjis Husain Dey, Utsho Swacha, Sharforaz Rahman |
| author_sort |
Shafique, Nadia |
| title |
Application of deep convolutional neural network in multiclass skin cancer classification using custom CNN architecture |
| title_short |
Application of deep convolutional neural network in multiclass skin cancer classification using custom CNN architecture |
| title_full |
Application of deep convolutional neural network in multiclass skin cancer classification using custom CNN architecture |
| title_fullStr |
Application of deep convolutional neural network in multiclass skin cancer classification using custom CNN architecture |
| title_full_unstemmed |
Application of deep convolutional neural network in multiclass skin cancer classification using custom CNN architecture |
| title_sort |
application of deep convolutional neural network in multiclass skin cancer classification using custom cnn architecture |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21934 |
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