Design of inception architecture for skin melanoma classification (original) (raw)
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Skin cancer classification using Inception Network
World Journal of Advanced Research and Reviews, 2024
Since skin disease is a universally recognized condition among humans, there has been a growing interest in utilizing intelligent systems to classify various skin ailments. This line of research in deep learning holds immense significance for dermatologists. However, accurately determining the presence of a disease is a formidable task due to the intricate nature of skin texture and the visual similarities between different diseases. To address this challenge, skin images undergo filtration to eliminate unwanted noise and undergo further processing to enhance the overall quality of the image. The primary purpose of this study is to construct a deep neural network-based model that is capable of automatically classifying several types of skin cancer as either melanoma or non-melanoma with a prominent level of accuracy. We propose an optimized Inception architecture, in which the InceptionNet model is enhanced with data augmentation and basic layers. The strategy that has been proposed enhances the model's capacity to deal with incomplete and inconsistent data. A dataset of 2637 skin images are used to demonstrate the benefits of the technique that has been proposed. We analyze the performance of the suggested method by looking at its precision, sensitivity, specificity, F1-score, and area under the ROC curve. Proposed InceptionNet provides an accuracy of 84.39% and 85.94%, respectively for Adam and Nadam optimizer. The training process in each subsequent layer exhibits a notable enhancement in effectiveness. An examination of this inquiry can assist experts in making early diagnoses, thereby providing them with insight into the nature of the infection and enabling them to initiate the necessary treatment, if deemed necessary.
Skin cancer classification computer system development with deep learning
2020
Melanoma is a deadly form of skin cancer that is often undiagnosed or misdiagnosed as a benign skin lesion. Its early detection is extremely important, since the life of patients with melanoma depends on accurate and early diagnosis of the disease. However, doctors often rely on personal experience and assess each patient's injuries based on a personal examination. Clinical studies allow us to get the accuracy of the diagnosis of melatoma from 65 to 80 percents, which was a good result for some time. However, modern research claims that the use of dermoscopic images in diagnosis significantly increases the accuracy of diagnosis of skin lesions. The visual differences between melanoma and benign skin lesions can be very small, making diagnosis difficult even for an expert doctor. Recent advances in the use of artificial intelligence methods in the analysis of medical images have made it possible to consider the development of intelligent medical diagnostic systems based on visualization as a very promising direction that will help the doctor in making more effective decisions about the health of patients and making a diagnosis at an early stage and in adverse conditions. In this paper, we propose an approach to solving the problem of classification of skin diseases, namely, melanoma at an early stage, based on deep learning. In particular, a solution to the problem of classification of a dermoscopic image containing either malignant or benign skin lesions is proposed. For this purpose, the deep neural network architecture was developed and applied to image processing. Computer experiments on the ISIC data set have shown that the proposed approach provides 92% accuracy on the test sample, which is significantly higher than other algorithms in this data set have shown.
Diagnosing Melanomas in Dermoscopy Images Using Deep Learning
Diagnostics
When it comes to skin tumors and cancers, melanoma ranks among the most prevalent and deadly. With the advancement of deep learning and computer vision, it is now possible to quickly and accurately determine whether or not a patient has malignancy. This is significant since a prompt identification greatly decreases the likelihood of a fatal outcome. Artificial intelligence has the potential to improve healthcare in many ways, including melanoma diagnosis. In a nutshell, this research employed an Inception-V3 and InceptionResnet-V2 strategy for melanoma recognition. The feature extraction layers that were previously frozen were fine-tuned after the newly added top layers were trained. This study used data from the HAM10000 dataset, which included an unrepresentative sample of seven different forms of skin cancer. To fix the discrepancy, we utilized data augmentation. The proposed models outperformed the results of the previous investigation with an effectiveness of 0.89 for Inception...
International Research Journal of Engineering and Technology, 2024
Melanoma represents a particularly aggressive form of skin cancer, necessitating prompt detection to mitigate mortality rates and minimize the invasiveness of treatment. Contemporary advances in computer-aided diagnosis (CAD) have leveraged sophisticated imaging techniques to enhance early-stage diagnosis of skin malignancies. This research aims to construct a web-based platform for the automated analysis of dermoscopic images to ascertain early melanoma presence. The implementation employs the Inception V3 model, a Convolutional Neural Network (CNN) architecture renowned for its efficacy in image classification tasks, specifically in medical imaging domains such as dermoscopy. This model facilitates the processes of image acquisition, preprocessing, segmentation, feature extraction, and classification. The web application, developed using Python, HTML, and CSS, showcases a streamlined interface for clinical application. Empirical evaluations reveal that the model achieves an accuracy range of 90-93%, underscoring its potential utility in clinical settings. This platform empowers users to swiftly identify skin abnormalities, thereby enhancing early diagnosis and preventive care, significantly contributing to advancements in dermatological oncology.
Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
Sensors, 2022
Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International S...
CLASSIFICATION OF MELANOMA FROM DERMOSCOPIC IMAGES USING DEEP LEARNING
Malignant melanoma is one of the rapidly increasing and deadly diseases in the world. Early diagnosis is of great importance for treating the disease. Accurate observation of skin lesions is needed for melanoma detection. Dermoscopy is a non-invasive technique for observation of skin lesion. Manual observation of dermoscopic images for classification of lesion as benign or malignant can be inaccurate and subjective. Therefore computer aided diagnosis (CAD) plays a significant role for assisting in melanoma detection.
IOSR Journal of Engineering (IOSRJEN), 2018
Melanoma is the most common type of skin cancer. At first, for the diagnosis of melanoma, clinical screening is performed and then diagnosis is made by clinical imaging. It is followed up by dermoscopic analysis, biopsy and histopathological examination. Early diagnosis is important in the treatment of melanoma. Automatic recognition of melanoma from dermoscopy images is a difficult task. Therefore, computer aided systems are recommended to reduce time ,cost and accuracy diagnosis. In this paper, a deep learning-based system is used to classify melanoma in color images taken from dermoscopy devices. With this system, differentiation from previous studies can be done with good accuracy without segmentation step and feature extraction. This system provides a significant advantage in hardware implementation. Because there are no pre-processing and segmentation steps. The International Skin Imaging Collaboration database for the designed system is used and includes 1483 training, 517 test data(ISIC). As a result of the classification of these data, the success rate is reached 86-85%.
Skin cancer classification dermatologist-level based on deep learning model
Acta Scientiarum. Technology
Medical image analysis is a significant burden for doctors, therefore, it is used to supplement image processing. Many medical images are assumed to be diagnosed as accurately as healthcare experts when the precision of image detection and recognition in an image processing approach matches that of a human being. Artificial Intelligence (AI) based predictive modelling is an important component of many healthcare solutions. This paper develops and implements a neural network-based method for skin cancer prediction to expose the neural network's strength in this field. This method determines which form of deep learning is best for diagnosing diseases with an accuracy exceeds human ability in terms of speed and accuracy, and determines the optimum number of layers and neurons in each layer for both Convolutional Neural network (CNN) and Deep Neural Network (DNN) to obtain the best possible precision. The results of the proposed method showed impressive results, especially for CNN. ...
JMIR Dermatology, 2020
Background Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole. Objective We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners. Methods We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model. Results We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall. Conclusions Based on our research, we conclude that deep learning algorithms are hi...
Elsevier, Informatics in medicine unlocked, 2020
Background: Skin cancer is a common form of cancer, and early detection increases the survival rate. Objective: To build deep learning models to classify dermal cell images and detect skin cancer. Methods: A model-driven architecture in the cloud, that uses deep learning algorithms in its core implementations, is used to construct models that assist in predicting skin cancer with improved accuracy. The study illustrates the method of building models and applying them to classify dermal cell images. Results: The deep learning models built here are tested on standard datasets, and the metric area under the curve of 99.77% was observed. Conclusions: A practitioner can use the model-driven architecture and quickly build the deep learning models to predict skin cancer.