Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Dyah Prabandari, Jasmine Iswarini; Sari, Christy Atika; Rachmawanto, Eko Hari; Irawan, Candra; Altamer, Bilal R.; Ayad Alkhafaji, Mohamed

doi:10.1109/isemantic59612.2023.10295362

Cited by 2 publications

(1 citation statement)

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“…CNN is a type of artificial neural network architecture that has become very important in image processing and pattern recognition [13]. CNNs are specifically designed to handle data in the form of images or grid data, such as medical images [14], and have produced extraordinary advances in various fields, including classification tasks [15]. The CNN architecture had the ability to automatically extract important features from images, such as edges, textures, and other visual patterns, which makes it very suitable for the classification of objects or categories in images [16], [17], [18], [19].…”

Section: Proposed Methods Based On Convolutional Neural Networkmentioning

confidence: 99%

Breast tumor classification using adam and optuna model optimization based on CNN architecture

Sari,

Rachmawanto,

Daniati

et al. 2024

J. Soft Comput. Explor.

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Breast cancer presents a significant challenge due to its complexity and the urgency of the intervention required to prevent metastasis and potential fatality. This article highlights the innovative application of Convolutional Neural Networks (CNN) in breast tumor classification, marking substantial progress in the field. The key to this advancement is the collaboration among medical professionals, scientists, and artificial intelligence experts, which maximizes the potential of technology. The research involved three phases of training with varying proportions of training data. The first training phase achieved the highest accuracy rate of 99.72%, with an average accuracy of 99.05% in all three phases. Metrics such as precision, recall, and F1 score were also highly satisfactory, underscoring the model's efficacy in accurately classifying breast tumors. Future research aims to develop more complex and precise predictive models by incorporating larger and more representative datasets. This progression promises to improve understanding, prevention, and management of breast cancer, offering hope for significant advances in 2024 and beyond.

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