Paper-Convolutional Neural Networks on Assembling Classification Models to Detect Melanoma (original) (raw)

Classifying Skin Cancer in Digital Images Using Convolutional Neural Network with Augmentation

2020

Skin cancer is a hazardous disease that can induces death if it is not taken care of immediately. The disease is hard to identified since the symptoms have similarities with other disease. An automatically classification system of skin cancer has been developed, but it still produced low accuracy. We use Convolutional Neural Network to enhance the accuracy of the classification. There are 2 main scenarios conducted in this research using HAM10000 dataset which has 7 classes. We compared ResNet and VGGNet architectures and obtained ResNet50 with augmentation as the best model with the accuracy of 99% and 99% macro avg.

Automatic Classification of Melanoma Skin Cancer with Deep Convolutional Neural Networks

AI

Melanoma skin cancer is one of the most dangerous types of skin cancer, which, if not diagnosed early, may lead to death. Therefore, an accurate diagnosis is needed to detect melanoma. Traditionally, a dermatologist utilizes a microscope to inspect and then provide a report on a biopsy for diagnosis; however, this diagnosis process is not easy and requires experience. Hence, there is a need to facilitate the diagnosis process while still yielding an accurate diagnosis. For this purpose, artificial intelligence techniques can assist the dermatologist in carrying out diagnosis. In this study, we considered the detection of melanoma through deep learning based on cutaneous image processing. For this purpose, we tested several convolutional neural network (CNN) architectures, including DenseNet201, MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16, VGG19, and GoogleNet, and evaluated the associated deep learning models on graphical processing units (GPUs). A dataset consisting of 71...

Enhancing melanoma skin cancer classification through data augmentation

TELKOMNIKA Telecommunication Computing Electronics and Control, 2024

Skin cancer is a dangerous and prevalent cancer illness. It is the abnormal growth of cells in the outermost of the skin. Currently, it has received tremendous attention, highlighting an urgent need to address this worldwide public health crisis. The purpose of this study is to propose a convolutional neural network (CNN) to help dermatology physicians in the inspection, identification, and diagnosis of skin cancer. More precisely, we offer an automated method that leverages deep learning techniques to categorize binary categories of skin lesions. Our technique enlarges skin cancer by utilizing data pre-processing and augmentation to address the imbalanced class problem. Subsequently, fine-tuning is conducted on the pre-trained models visual geometry group (VGG-19) and MobileNetV2 to extract and classify the image features using transfer learning. The model is tested on the society for imaging informatics in medicine international skin imaging collaboration (SIIM-ISIC) 2020 dataset and achieved an accuracy of 95.16%, sensitivity of 90.83%, specificity of 99.2%, area under curve (AUC) of 97.57%, and precision of 99.06%. The proposed model based on MobileNetV2 outperforms the other techniques.

Skin Lesion Classification Using Convolutional Neural Network for Melanoma Recognition

2020

Skin cancer, also known as melanoma, is generally diagnosed visually from the dermoscopic images, which is a tedious and time-consuming task for the dermatologist. Such a visual assessment, via the naked eye for skin cancers, is a challenging and arduous due to different artifacts such as low contrast, various noise, presence of hair, fiber, and air bubbles, etc. This article proposes a robust and automatic framework for the Skin Lesion Classification (SLC), where we have integrated image augmentation, Deep Convolutional Neural Network (DCNN), and transfer learning. The proposed framework was trained and tested on publicly available IEEE International Symposium on Biomedical Imaging (ISBI)-2017 dataset. The obtained average area under the receiver operating characteristic curve (AUC), recall, precision, and F1-score are respectively 0.87, 0.73, 0.76, and 0.74 for the SLC. Our experimental studies for lesion classification demonstrate that the proposed approach can successfully disti...

Detection of Melanoma Skin Cancer using Convolutional Neural Network algorithm

2020

Skin Cancer, a health issue which might cause severe consequences if not detected and controlled properly. Since there is a huge evolution in the health sector because of development in computer technologies, it is possible to analyze images efficiently and make correct decisions. Deep learning algorithms can be used for analyzing dermoscopic images by learning features of images in an incremental manner. Aim of our proposed method is to categorize skin lesion image as Benign or Melanoma and also to study the performance of Convolutional Neural Network algorithm using data augmentation technique and without data augmentation technique. The proposed method uses dataset from ISIC archive 2019. Steps involved in the proposed method are Image Pre-Processing, Image Segmentation and Image Classification. Initially, Image Pre-Processing algorithm is performed on skin lesion image. Image Segmentation algorithm is used to obtain Region of Interest (ROI) from pre-processed image. Then, Convol...

Accurate skin cancer diagnosis based on convolutional neural networks

Indonesian Journal of Electrical Engineering and Computer Science, 2022

Although melanoma is not the most common type of skin cancer, it is supposed to extend to other areas of the body if not early diagnosed. Melanoma is the deadliest form of skin cancer and accounts for about 75% of deaths associated with skin cancer. The present study introduces an automated technique for skin cancer prediction, detection, and diagnosis including trending noninvasive and nonionizing techniques that combines deep learning methods to diagnose melanoma with high accuracy. Computer-aided diagnosis (CAD) using medical images is utilized to distinguish benign and malignant tumors, which can assist physicians in early identification of symptoms, thus lowering the mortality rate. The CAD system consists of four phases; detection of the region of interest (RoI), using data augmentation techniques, processing RoI using convolutional neural network (CNN) to extract the most important features, and finally the extracted CNN features are input to a support vector machine (SVM) classifier to decode the two classes benign (B) and malignant (M). Two datasets, ISIC and CPTAC-CM, were utilized to train the CNNs. GoogleNet, ResNet-50, AlexNet, and VGG19 were investigated and compared. The accuracy of the proposed CAD system has reached 99.8% for ISIC database and 99.9% for CPTAC-CM database.

Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks

Multimedia Tools and Applications, 2020

Automatic classification of color images of skin helps clinicians and dermatologists in examining and investigating skin melanoma. In this paper, a new deep convolutional neural network-based classification method is proposed. The proposed method consists of three main steps. First, the input color images of skin are preprocessed where the region of interest (ROI) are segmented. Second, the segmented ROI images are augmented using rotation and translation transformations. Third, different deep convolutional neural network (DCNN) architectures such as Alex-net, ResNet101, and GoogleNet are utilized. The last three layers are dropped out and replaced with new layers to be more appropriate with the task of lesion classification. The performance of the proposed method has been evaluated using three different datasets, MED-NODE, DermIS & DermQuest and ISIC 2017. The proposed DCNN have fine-tuned and trained using 85%, tested and verified using 15% of the overall datasets. The proposed method significantly improved the classification process especially with modified GoogleNet where the classification accuracy was 99.29%, 99.15%, and 98.14% for MED-NODE, Der-mIS & DermQuest, and ISIC 2017 respectively.

Skin Lesion Classification Based on Convolutional Neural Networks

2019

Melanoma causes the majority of skin cancer deaths. The population level of melanoma has increased over the past 30 years. It kills around 9.320 people in the US every year. Melanoma can often be found early, when it is most likely to be cured. Medical diagnoses using digital imaging with machine learning methods have become popular because of their ability to recognize patterns in digital images. Image diagnosis accuracy allows disease cured at an early stage. This paper proposes a simulation that can be used for early detection of skin cancer that can help dermatologists to distinguish melanomas from other pigmented lesions on the skin. Some researchers have developed a system using machine learning algorithms used to classify skin lesions from dermoscopy images of human skin. In this study, we proposed Convolutional Neural Network (CNN) to our model. CNN is very efficient for image processing because feature extractors can be optimized, applied to each feature image position. The...

Optimized Convolutional Neural Network Models for Skin Lesion Classification

Computers, Materials & Continua

Skin cancer is one of the most severe diseases, and medical imaging is among the main tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models based on DenseNet-201, Inception-ResNet-V2, and Inception-V3 are proposed and compared using the HAM10000 dataset. The results obtained by the three models demonstrate accuracies of 98%, 97%, and 96%, respectively. Finally, the best model is tested on the ISIC 2019 dataset showing an accuracy of 93%. The proposed methodology using CNN represents a helpful tool to accurately diagnose skin cancer disease.

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...