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An AI Based skin cancer detection system utilizing Raspberry Pi. 6

By: Jhaniella B. Quinlog, Lhorrd Lester P. Quiroz, Ophelia Eunice J. Romero, Migi Kirsten B. Viray. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 4541346Edition: Description: Content type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: ABSTRACT: Skin cancer is one of the most common and dangerous abnormalities that globally claim numerous lives due to high morbidity, which is particularly relevant for Asian countries such as the Philippines. This study presents the implementation of Rasberry Pi with MobileNetv2 Convolutional Neural Network (CNN) as a tool to classify skin lesions: melanoma, basal cell carcinoma, and squamous cell carcinoma. The research dataset is composed of images of varying skin cancer and augmented to increase the dataset's size and diversity to achieve effective model training. The study examines the performance of a skin cancer detection device its training process, evaluation, and device testing. The study found no significant difference between training and validation accuracies, indicating the model's ability to generalize. This study shows that the device has the ability to detect a wide range of skin cancer types and non-cancerous lesions, with an average accuracy of 96.71% for melanoma, bsal cell carcinoma, squamous cell carcinoma, and non-cancerous lesions. In addition, the device stands out due to its use of the latest Rasberry Pi 5 model. Thus, this study offers an efficient solution for detecting skin cancer and showcased better precision and accuracy when compared to previous studies. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	TK9965 .Q56 2024 (Browse shelf)	Available		FT7914

Total holds: 0

Undergraduate Thesis : (Bachelor of Science in Computer Science) _ Pamantasan ng Lungsod ng Maynila, 2024. 56

ABSTRACT: Skin cancer is one of the most common and dangerous abnormalities that globally claim numerous lives due to high morbidity, which is particularly relevant for Asian countries such as the Philippines. This study presents the implementation of Rasberry Pi with MobileNetv2 Convolutional Neural Network (CNN) as a tool to classify skin lesions: melanoma, basal cell carcinoma, and squamous cell carcinoma. The research dataset is composed of images of varying skin cancer and augmented to increase the dataset's size and diversity to achieve effective model training. The study examines the performance of a skin cancer detection device its training process, evaluation, and device testing. The study found no significant difference between training and validation accuracies, indicating the model's ability to generalize. This study shows that the device has the ability to detect a wide range of skin cancer types and non-cancerous lesions, with an average accuracy of 96.71% for melanoma, bsal cell carcinoma, squamous cell carcinoma, and non-cancerous lesions. In addition, the device stands out due to its use of the latest Rasberry Pi 5 model. Thus, this study offers an efficient solution for detecting skin cancer and showcased better precision and accuracy when compared to previous studies.

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