Transfer Learning Techniques for Skin Cancer Classification

<p>Skin cancer is an example of the most dangerous disease. Early diagnosis of skin cancer can save many people’s lives. Manual classification methods are time-consuming and costly. Deep learning has been proposed for the automated classification of skin cancer. Although deep learning showed impressive performance in several medical imaging tasks, it requires a big number of images to achieve a good performance. The skin cancer classification task suffers from providing deep learning with sufficient data due to the expensive annotation process and required experts. One of the most used solutions is transfer learning of pre-trained models of the ImageNet dataset. However, the learned features of pre-trained models are different from skin cancer image features. To end this, we introduce a novel approach of transfer learning by training the pre-trained models of the ImageNet (VGG, GoogleNet, and ResNet50) on a large number of unlabelled skin cancer images, first. We then train them on a small number of labeled skin images. Our experimental results proved that the proposed method is efficient by achieving an accuracy of 84% with ResNet50 when directly trained with a small number of labeled skin and 93.7% when trained with the proposed approach.</p>

The inhibition of healthy cells creating improper controlling process of the human body system indicates the occurrence of growth of cancerous cells. The cluster of such cells leads to the development of tumor. The observation of this type of abnormal skin pigmentation is done using an effective tool called Dermoscopy. However, these dermatoscopic images possess a great challenge for diagnosis. Considering the characteristics of dermatoscopic images, transfer learning is an appropriate approach of automatically classifying the images based on the respective categories. An automatic identification of skin cancer not only saves human life but also helps in detecting its growth at an earlier stage which saves medical practitioner’s effort and time. A newly predicted model has been proposed for classifying the skin cancer as benign or malignant by DCNN with transfer learning and its pre-trained models such as VGG 16, VGG 19, ResNet 50, ResNet 101, and Inception V3. The proposed methodology aims at examining the efficiency of pre-trained models and transfer learning approach for the classification tasks and opens new dimensions of research in the field of medicines using imaging technique which can be implementable in real-time applications.

Facial Segmentation in Deepfake Classification: a Transfer Learning Approach

• The finite element-integrated neural network (FEINN) is enhanced by transfer learning strategy. • The impacts of the FEM mesh on FEINN are investigated and fully discussed. • Scale transfer learning, material transfer learning, load transfer learning strategies are evaluated. • FEINN is successfully accelerated in solving elastic, elastoplastic, multiple material boundary value problems. Physics informed neural networks (PINNs) have attracted increasing attention in computational solid mechanics due to their success in solving complex partial differential equations (PDEs). Nevertheless, the low efficiency and precision always hinder the application of PINNs in boundary value problems. To address this issue, this study proposed a transfer learning enhanced hybrid framework that integrates the finite element method with PINNs to accelerate the training process. The finite element-integrated neural network framework (FEINN) is first introduced, leveraging finite elements for domain discretization and the weak-form governing equation for defining the loss function. A mesh parametric study is subsequently conducted, aiming to identify the optimal discretization configuration by exploring various element sizes, element types, and orders of shape functions. Furthermore, various transfer learning strategies are proposed and fully evaluated to improve the training efficiency and precision of FEINN, including scale transfer learnings (STLs) from coarse mesh to refine mesh and from small domain to large domain, material transfer learnings (MTLs) from elastic material to elastoplastic material and from elastic material to elastic material problems, as well as load transfer learnings (LTLs) form displacement load condition to force load condition. A series of experiments are conducted to showcase the effectiveness of FEINN, identifying the most efficient discretization configuration and validating the efficacy of transfer learning strategies across elastic, elastoplastic, and multi-material scenarios. The results indicate that the element type and size, and shape function order have significant impacts on training efficiency and accuracy. Moreover, the transfer learning techniques can significantly improve the accuracy and training efficiency of FEINN.

Climate and other environmental humans are being more exposed to the Sun's ultraviolet(UV) rays. This exposure has increased the chances of humans developing skin cancer. The early detection of skin cancer is vital for curing this disease. The recent development in Neural Networks and state of the art deep learning techniques have an essential role in detecting skin cancer. In this paper, we extensively go through the deep neural network architectures and compare their results. We have tried to propose an analysis report using the state-of-the-art transfer and comparing their prediction capabilities to detect Skin Cancer by testing it on the HAM1000 dataset. We propose a solution by using transfer learning which have very heavily trained dense trainable layers with weights from imagenet. Finally, we show that among all the models, Resnet50, U-net, Densenet121, MOBILENETV1, InceptionV3 and U Net achieved the highest accuracy with 91% test accuracy becoming the top approach. To draw comparison, we have also used CNN architecture models which was previously structured using Keras Sequential API. We see the newer dense transfer learning models provide better testing accuracy. Hence, this paper aims to detect skin in its early stage to start treatment and hopefully save valuable lives in the process.

Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals

Today breast cancer is the leading type of cancer among women undergoing cancer screening. A slight delay in detecting and diagnosing this disease may result in irreversible convolutions. Histopathological images from the biopsy examination present a large amount of structural information that can signi cantly improve the prognosis for breast cancer. The pathological analysis, which involves the microscopic examination of the histopathological slides, is a challenging task. An automated computer-aided detection (CAD) procedure is inevitable, as it may decrease the pathologist examination time and help detect the disease at an early stage. Lately, deep learning methods using arti cial neural networks are consistently in use to improve the performance of CAD methods. A common practice among recent studies is to use the transfer learning approach of training deep neural network architectures. Transfer Learning is an established learning approach that facilitates a deep neural network to train quickly on a speci c dataset and resolve an interdisciplinary problem. Deep Learning methods employing the transfer learning approach have provided highly competitive results on the datasets consisting of the whole slide images, which are captured generally at high resolutions. However, the performance is not remarkably appreciable on the small and low-resolution image datasets, in particular the datasets that include patch samples. In this direction, the present study proposes a novel domain-speci c learning strategy, Breast Histo-Fusion, which aims to detect breast cancer even from images of low resolution and small size. Further, four state-of-the-art deep CNNs (AlexNet, VGG19, ResNet, and DenseNet) are trained using both learning approaches: Transfer Learning and Histo-Fusion on the IDC dataset. The proposed Histo-Fusion learning approach has improved the discriminating abilities and performance of each deep CNN by providing better results of (AlexNet -95.75%, VGG19-95.96%, ResNet34-96.17%, ResNet50-96.67%, and DenseNet121-97.49%) compared to (AlexNet -90.41, VGG19-90.51%, ResNet34-90.83%, ResNet50-92.27%, and DenseNet121-93.05%) using the transfer learning strategy. As a result, the procedure can help expert pathologists to perform accurate diagnoses and reduce false-positive rates.

Skin cancer diagnosis challenges dermatologists due to its complex visual variations across diagnostic categories. Convolutional neural networks (CNNs), specifically the Efficient Net B0-B7 series, have shown superiority in multiclass skin cancer classification. This study addresses the limitations of visual examination by presenting a tailored preprocessing pipeline designed for Efficient Net models. Leveraging transfer learning with pre-trained ImageNet weights, the research aims to enhance diagnostic accuracy in an imbalanced multiclass classification context. The study develops a specialized image preprocessing pipeline involving image scaling, dataset augmentation, and artifact removal tailored to the nuances of Efficient Net models. Using the Efficient Net B0-B7 dataset, transfer learning fine-tunes CNNs with pre-trained ImageNet weights. Rigorous evaluation employs key metrics like Precision, Recall, Accuracy, F1 Score, and Confusion Matrices to assess the impact of transfer learning and fine-tuning on each Efficient Net variant's performance in classifying diverse skin cancer categories. The research showcases the effectiveness of the tailored preprocessing pipeline for Efficient Net models. Transfer learning and fine-tuning significantly enhance the models' ability to discern diverse skin cancer categories. The evaluation of eight Efficient Net models (B0-B7) for skin cancer classification reveals distinct performance patterns across various cancer classes. While the majority class, Benign Kertosis, achieves high accuracy (>87%), challenges arise in accurately classifying Eczema classes. Melanoma, despite its minority representation (2.42% of images), attains an average accuracy of 80.51% across all models. However, suboptimal performance is observed in predicting warts molluscum (90.7%) and psoriasis (84.2%) instances, highlighting the need for targeted improvements in accurately identifying specific skin cancer types. The study on skin cancer classification utilizes EfficientNets B0-B7 with transfer learning from ImageNet weights. The pinnacle performance is observed with EfficientNet-B7, achieving a groundbreaking top-1 accuracy of 84.4% and top-5 accuracy of 97.1%. Remarkably efficient, it is 8.4 times smaller than the leading CNN. Detailed per-class classification exactitudes through Confusion Matrices affirm its proficiency, signaling the potential of EfficientNets for precise dermatological image analysis.

Abstract. In recent years, the increasing complexity of spatial data related to landscape heritage and urban legacies has led to a growing focus in research on enhancing the efficiency of management processes while simultaneously increasing their level of automation. In this context, a highly relevant solution is the use of so-called artificial intelligence, and more specifically, predictive models generated through Deep Learning techniques. However, a commonly observed critical issue concerns the limited generalisation capability of these models, which often fail to accurately recognise features in datasets that differ from those used during the training phase. In the present contribution, starting from a predictive model trained on airborne LiDAR point clouds belonging to a regional dataset (Sardinia, Italy), a transfer learning approach is proposed using new data (derived from the ISPRS benchmark dataset for semantic segmentation of Hessigheim – H3D) to improve the generalisation capabilities of the model. In order to assess the suitability of the proposed transfer learning strategy, a comparison between the classification performed by the original predictive model and the fine-tuned predictive model has been performed. Furthermore, the evaluation metrics have been calculated, evaluated, and discussed to quantitatively assess the improvement in terms of results, performance, and absolute gain between the different models tested. The proposed workflow supports scalable landscape and urban heritage monitoring by reducing human intervention in data management workflows while maintaining semantic consistency in available airborne laser scanner (ALS) data processing. While the contribution presents class limitations due to the constraints of the training data used for the first predictive model, the study demonstrates how transfer learning strategies can enhance the performance of semantic segmentation models when handling existing sparse data. This aligns well with scientific community efforts toward automated, efficient, and scalable heritage monitoring and documentation using remote sensing techniques.

Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, deep learning algorithms have been proposed. Deep learning algorithms have shown diagnostic efficacy comparable to dermatologists in the discipline of images-based skin lesion diagnosis in recent research articles. This work proposes a novel deep learning algorithm to detect skin cancer. The proposed CAD-Skin system detects and classifies skin lesions using deep convolutional neural networks and autoencoders to improve the classification efficiency of skin cancer. The CAD-Skin system was designed and developed by the use of the modern preprocessing approach, which is a combination of multi-scale retinex, gamma correction, unsharp masking, and contrast-limited adaptive histogram equalization. In this work, we have implemented a data augmentation strategy to deal with unbalanced datasets. This step improves the model's resilience to different pigmented skin conditions and avoids overfitting. Additionally, a Quantum Support Vector Machine (QSVM) algorithm is integrated for final-stage classification. Our proposed CAD-Skin enhances category recognition for different skin disease severities, including actinic keratosis, malignant melanoma, and other skin cancers. The proposed system was tested using the PAD-UFES-20-Modified, ISIC-2018, and ISIC-2019 datasets. The system reached accuracy rates of 98%, 99%, and 99%, consecutively, which is higher than state-of-the-art work in the literature. The minimum accuracy achieved for certain skin disorder diseases reached 97.43%. Our research study demonstrates that the proposed CAD-Skin provides precise diagnosis and timely detection of skin abnormalities, diversifying options for doctors and enhancing patient satisfaction during medical practice.

Skin cancers are one of the most common cancers in the world. Early detections and treatments of skin cancers can greatly improve the survival rates of patients. In this paper, a skin lesions classification system is developed with deep convolutional neural networks of ResNet50, which may help dermatologists to recognize skin cancers earlier. We utilize the ResNet50 as a pre-trained model. Then, by transfer learning, it is trained on our skin lesions dataset. Image preprocessing and dataset balancing methods are used to increase the accuracy of the classification model. In classification of skin diseases, our model achieves an overall accuracy of 83.74% on nine-class skin lesions. The experimental results show an impressive effect of the ResNet50 model in finegrained skin lesions classification and skin cancers recognition.

The amount of malware infecting computers and other communication devices and migrating across the internet has greatly increased over the years. Numerous methods and procedures have been put forth up to this point to find and eliminate these hostile agents. However, a lot of malwares is still being developed, which can get past some cutting-edge malware detection algorithms, as new and automated malware production techniques emerge. Consequently, there is a need for the classification and detection of these antagonistic agents that have the potential to compromise the security of individuals, businesses, and a wide range of other digital assets. To develop a new improved approach for efficient zero-day malware detection, there is a compelling need to reduce bias and objectively assess these methods. This study focuses on investigating transfer learning strategies for malware picture classification, including AlexNet, VGG16, VGG19, GoogLeNet, and ResNet. Here, malware binaries are turned into grayscale images before being processed using models for classification based on transfer learning. As transfer learning uses pre-trained models, the primary objective is to save training time. In addition, an effective model for malware classification will be built in order to obtain performance that is unbiased.

Gene regulation is responsible for controlling numerous physiological functions and dynamically responding to environmental fluctuations. Reconstructing the human network of gene regulatory interactions is thus paramount to understanding the cell functional organization across cell types, as well as to elucidating pathogenic processes and identifying molecular drug targets. Although significant effort has been devoted towards this direction, existing computational methods mainly rely on gene expression levels, possibly ignoring the information conveyed by mechanistic biochemical knowledge. Moreover, except for a few recent attempts, most of the existing approaches only consider the information of the organism under analysis, without exploiting the information of related model organisms. We propose a novel method for the reconstruction of the human gene regulatory network, based on a transfer learning strategy that synergically exploits information from human and mouse, conveyed by gene-related metabolic features generated in silico from gene expression data. Specifically, we learn a predictive model from metabolic activity inferred via tissue-specific metabolic modelling of artificial gene knockouts. Our experiments show that the combination of our transfer learning approach with the constructed metabolic features provides a significant advantage in terms of reconstruction accuracy, as well as additional clues on the contribution of each constructed metabolic feature. The method, the datasets and all the results obtained in this study are available at: https://doi.org/10.6084/m9.figshare.c.5237687. Supplementary data are available at Bioinformatics online.

Skin Cancer is an extensive and possibly dangerous disorder that requires early detection for effective treatment. Add specific global statistics on skin cancer prevalence and mortality to emphasize the importance of early detection. Example: “Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. Manual skin cancer screening is both time-intensive and expensive. Deep learning (DL) techniques have shown exceptional performance in various applications and have been applied to systematize skin cancer diagnosis. However, training DL models for skin cancer diagnosis is challenging due to limited available data and the risk of overfitting. Traditionally approaches have High computational costs, a lack of interpretability, deal with numerous hyperparameters and spatial variation have always been problems with machine learning (ML) and DL. An innovative method called adaptive learning has been developed to overcome these problems. In this research, we advise an intelligent computer-aided system for automatic skin cancer diagnosis using a two-stage transfer learning approach and Pre-trained Convolutional Neural Networks (CNNs). CNNs are well-suited for learning hierarchical features from images. Annotated skin cancer photographs are utilized to detect ROIs and reset the initial layer of the pre-trained CNN. The lower-level layers learn about the characteristics and patterns of lesions and unaffected areas by fine-tuning the model. To capture high-level, global features specific to skin cancer, we replace the fully connected (FC) layers, responsible for encoding such features, with a new FC layer based on principal component analysis (PCA). This unsupervised technique enables the mining of discriminative features from the skin cancer images, effectively mitigating overfitting concerns and letting the model adjust structural features of skin cancer images, facilitating effective detection of skin cancer features. The system shows great potential in facilitating the initial screening of skin cancer patients, empowering healthcare professionals to make timely decisions regarding patient referrals to dermatologists or specialists for further diagnosis and appropriate treatment. Our advanced adaptive fine-tuned CNN approach for automatic skin cancer diagnosis offers a valuable tool for efficient and accurate early detection. By leveraging DL and transfer learning techniques, the system has the possible to transform skin cancer diagnosis and improve patient outcomes.

Skin cancer has emerged as a significant global health concern, with a growing incidences worldwide. Timely detection of skin cancer is pivotal, as early diagnosis greatly enhances the prospects of successful treatment. While skin cancer is commonly associated with sun-exposed areas, it can also manifest in less typical regions. Alarmingly, over 123,000 melanoma and 30,00,000 non-melanoma cases are reported annually across the globe. Given the rapid increase in skin cancer cases, early identification of both cancerous and non-cancerous lesions is imperative. In response to the limitations faced by dermatologists in providing consistently accurate diagnoses, there is a pressing need for an automated and efficient diagnostic system. This study presents an innovative and automated approach for skin cancer classification, achieving superior evaluation metrics and enhanced accuracy compared to prior studies and expert dermatologists. Our proposed Convolutional Neural Network (CNN) model, augmented by leveraging pre-trained models, attains an accuracy rate of 94%, with the highest among the pre-trained models reaching 97%. This research offers a promising avenue to revolutionize skin cancer diagnosis, facilitating early intervention and improved patient outcomes.