Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network

Accurate bladder cancer diagnosis using ensemble deep leaning

There are an estimated 1.3 million cases of cancer globally each year, making it one of the most serious types of urinary tract cancer. The methods used today for diagnosing and monitoring bladder cancer are intrusive, costly, and time-consuming. In clinical practice, invasive biopsy followed by histological examination continues to be the gold standard for diagnosis. Bladder cancer biomarkers have been used in expensive diagnostic tests created recently, however their reliability is limited by their high rates of false positives and false negatives. The potential and use of artificial intelligence in urological diseases have been the subject of several research, as interest in artificial intelligence in medicine has grown recently. In this paper, a new bladder cancer model called Ensemble Deep Learning (EDL) will be provided to accurately diagnose patients. Outlier rejection is used to filter data using the interquartile range (IQR) then the image diagnosis. The proposed EDL consists of three deep learning algorithms, which are; Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), and a new deep learning method called Explainable Deep Learning (XDL) that depends on Guided Gradient Weighted Class Activation Map (Guided Grad-CAM). In fact, Guided Grad-CAM enables doctor to understand the diagnose. A new voting mechanism will be used to integrate the results of all three methods to produce the final result to accurately diagnose bladder cancer cases. In fact, the used voting method depends on using majority voting based on two different scenarios according to the results of CNN, GAN, and XDL. If these three methods give the same class category, then the final diagnosis will be this class category. On the other hand, if the three methods give different class category, then the final result will be followed by the accuracy of each class. The proposed EDL model was tested after several trials. The results have proved that EDL model is more efficient and more accurate to diagnose bladder cancer disease. It introduced the highest accuracy results and the lowest error results as well as execution time.

Skin-lesion segmentation using boundary-aware segmentation network and classification based on a mixture of convolutional and transformer neural networks

Background

Skin cancer is one of the most prevalent cancers worldwide. In the clinical domain, skin lesions such as melanoma detection are still a challenge due to occlusions, poor contrast, poor image quality, and similarities between skin lesions. Deep-/machine-learning methods are used for the early, accurate, and efficient detection of skin lesions. Therefore, we propose a boundary-aware segmentation network (BASNet) model comprising prediction and residual refinement modules.

Materials and methods

The prediction module works like a U-Net and is densely supervised by an encoder and decoder. A hybrid loss function is used, which has the potential to help in the clinical domain of dermatology. BASNet handles these challenges by providing robust outcomes, even in suboptimal imaging environments. This leads to accurate early diagnosis, improved treatment outcomes, and efficient clinical workflows. We further propose a compact convolutional transformer model (CCTM) based on convolution and transformers for classification. This was designed on a selected number of layers and hyperparameters having two convolutions, two transformers, 64 projection dimensions, tokenizer, position embedding, sequence pooling, MLP, 64 batch size, two heads, 0.1 stochastic depth, 0.001 learning rate, 0.0001 weight decay, and 100 epochs.

Results

The CCTM model was evaluated on six skin-lesion datasets, namely MED-NODE, PH2, ISIC-2019, ISIC-2020, HAM10000, and DermNet datasets, achieving over 98% accuracy.

Conclusion

The proposed model holds significant potential in the clinical domain. Its ability to combine local feature extraction and global context understanding makes it ideal for tasks like medical image analysis and disease diagnosis.

Active Learning with Particle Swarm Optimization for Enhanced Skin Cancer Classification Utilizing Deep CNN Models

Skin cancer is a critical global health issue, with millions of non-melanoma and melanoma cases diagnosed annually. Early detection is essential to improving patient outcomes, yet traditional deep learning models for skin cancer classification are often limited by the need for large, annotated datasets and extensive computational resources. The aim of this study is to address these limitations by proposing an efficient skin cancer classification framework that integrates active learning (AL) with particle swarm optimization (PSO). The AL framework selectively identifies the most informative unlabeled instances for expert annotation, minimizing labeling costs while optimizing classifier performance. PSO, a nature-inspired metaheuristic algorithm, enhances the selection process within AL, ensuring the most relevant data points are chosen. This method was applied to train multiple Convolutional Neural Network (CNN) models on the HAM10000 skin lesion dataset. Experimental results demonstrate that the proposed AL-PSO approach significantly improves classification accuracy, with the Least Confidence strategy achieving approximately 89.4% accuracy while using only 40% of the labeled training data. This represents a substantial improvement over traditional approaches in terms of both accuracy and efficiency. The findings indicate that the integration of AL and PSO can accelerate the adoption of AI in clinical settings for skin cancer detection. The code for this study is publicly available at ( https://github.com/Sayantani-31/AL-PSO ).

HD-Former: A hierarchical dependency Transformer for medical image segmentation

Medical image segmentation is a compelling fundamental problem and an important auxiliary tool for clinical applications. Recently, the Transformer model has emerged as a valuable tool for addressing the limitations of convolutional neural networks by effectively capturing global relationships and numerous hybrid architectures combining convolutional neural networks (CNNs) and Transformer have been devised to enhance segmentation performance. However, they suffer from multilevel semantic feature gaps and fail to account for multilevel dependencies between space and channel. In this paper, we propose a hierarchical dependency Transformer for medical image segmentation, named HD-Former. First, we utilize a Compressed Bottleneck (CB) module to enrich shallow features and localize the target region. We then introduce the Dual Cross Attention Transformer (DCAT) module to fuse multilevel features and bridge the feature gap. In addition, we design the broad exploration network (BEN) that cascades convolution and self-attention from different percepts to capture hierarchical dense contextual semantic features locally and globally. Finally, we exploit uncertain multitask edge loss to adaptively map predictions to a consistent feature space, which can optimize segmentation edges. The extensive experiments conducted on medical image segmentation from ISIC, LiTS, Kvasir-SEG, and CVC-ClinicDB datasets demonstrate that our HD-Former surpasses the state-of-the-art methods in terms of both subjective visual performance and objective evaluation. Code: https://github.com/barcelonacontrol/HD-Former.

Exploring the influence of transformer-based multimodal modeling on clinicians' diagnosis of skin diseases: A quantitative analysis

Objectives

The study aimed to propose a multimodal model that incorporates both macroscopic and microscopic images and analyze its influence on clinicians' decision-making with different levels of experience.

Methods

First, we constructed a multimodal dataset for five skin disorders. Next, we trained unimodal models on three different types of images and selected the best-performing models as the base learners. Then, we used a soft voting strategy to create the multimodal model. Finally, 12 clinicians were divided into three groups, with each group including one director dermatologist, one dermatologist-in-charge, one resident dermatologist, and one general practitioner. They were asked to diagnose the skin disorders in four unaided situations (macroscopic images only, dermatopathological images only, macroscopic and dermatopathological images, all images and metadata), and three aided situations (macroscopic images with model 1 aid, dermatopathological images with model 2&3 aid, all images with multimodal model 4 aid). The clinicians' diagnosis accuracy and time for each diagnosis were recorded.

Results

Among the trained models, the vision transformer (ViT) achieved the best performance, with accuracies of 0.8636, 0.9545, 0.9673, and AUCs of 0.9823, 0.9952, 0.9989 on the training set, respectively. However, on the external validation set, they only achieved accuracies of 0.70, 0.90, and 0.94, respectively. The multimodal model performed well compared to the unimodal models, achieving an accuracy of 0.98 on the external validation set. The results of logit regression analysis indicate that all models are helpful to clinicians in making diagnostic decisions [Odds Ratios (OR) > 1], while metadata does not provide assistance to clinicians (OR < 1). Linear analysis results indicate that metadata significantly increases clinicians' diagnosis time (P < 0.05), while model assistance does not (P > 0.05).

Conclusions

The results of this study suggest that the multimodal model effectively improves clinicians' diagnostic performance without significantly increasing the diagnostic time. However, further large-scale prospective studies are necessary.

SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network

Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet's performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet's effectiveness, as it consistently outperformed both benchmarks across all datasets.

Skin Lesion Segmentation in Dermoscopic Images with Noisy Data

We propose a deep learning approach to segment the skin lesion in dermoscopic images. The proposed network architecture uses a pretrained EfficientNet model in the encoder and squeeze-and-excitation residual structures in the decoder. We applied this approach on the publicly available International Skin Imaging Collaboration (ISIC) 2017 Challenge skin lesion segmentation dataset. This benchmark dataset has been widely used in previous studies. We observed many inaccurate or noisy ground truth labels. To reduce noisy data, we manually sorted all ground truth labels into three categories - good, mildly noisy, and noisy labels. Furthermore, we investigated the effect of such noisy labels in training and test sets. Our test results show that the proposed method achieved Jaccard scores of 0.807 on the official ISIC 2017 test set and 0.832 on the curated ISIC 2017 test set, exhibiting better performance than previously reported methods. Furthermore, the experimental results showed that the noisy labels in the training set did not lower the segmentation performance. However, the noisy labels in the test set adversely affected the evaluation scores. We recommend that the noisy labels should be avoided in the test set in future studies for accurate evaluation of the segmentation algorithms.

SkinNet‐ENDO: Multiclass skin lesion recognition using deep neural network and Entropy‐Normal distribution optimization algorithm with ELM

The early diagnosis of skin cancer through clinical methods reduces the human mortality rate. The manual screening of dermoscopic images is not an efficient procedure; therefore, researchers working in the domain of computer vision employed several algorithms to classify the skin lesion. The existing computerized methods have a few drawbacks, such as low accuracy and high computational time. Therefore, in this work, we proposed a novel deep learning and Entropy‐Normal Distribution Optimization Algorithm with extreme learning machine (NDOEM)‐based architecture for multiclass skin lesion classification. The proposed architecture consists of five fundamental steps. In the first step, two contrast enhancement techniques including hybridization of mathematical formulation and convolutional neural network are implemented prior to data augmentation. In the second step, two pre‐trained deep learning models, EfficientNetB0 and DarkNet19, are fine‐tuned and retrained through the transfer learning. In the third step, features are extracted from the fine‐tuned models and later the most discriminant features are selected based on novel Entropy‐NDOELM algorithm. The selected features are finally fused using a parallel correlation technique in the fourth step to generate the result feature vectors. Finally, the resultant features are again down‐sampled using the proposed algorithm and the resultant features are passed to the extreme learning machine (ELM) for the final classification. The simulations are conducted on three publicly available datasets as HAM10000, ISIC2018, and ISIC2019 to achieving an accuracy of 95.7%, 96.3%, and 94.8% respectively.

Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search

Recently, pre-trained deep learning (DL) models have been employed to tackle and enhance the performance on many tasks such as skin cancer detection instead of training models from scratch. However, the existing systems are unable to attain substantial levels of accuracy. Therefore, we propose, in this paper, a robust skin cancer detection framework for to improve the accuracy by extracting and learning relevant image representations using a MobileNetV3 architecture. Thereafter, the extracted features are used as input to a modified Hunger Games Search (HGS) based on Particle Swarm Optimization (PSO) and Dynamic-Opposite Learning (DOLHGS). This modification is used as a novel feature selection to alloacte the most relevant feature to maximize the model's performance. For evaluation of the efficiency of the developed DOLHGS, the ISIC-2016 dataset and the PH2 dataset were employed, including two and three categories, respectively. The proposed model has accuracy 88.19% on the ISIC-2016 dataset and 96.43% on PH2. Based on the experimental results, the proposed approach showed more accurate and efficient performance in skin cancer detection than other well-known and popular algorithms in terms of classification accuracy and optimized features.

Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull.

Multiscale Feature Fusion for Skin Lesion Classification

Skin cancer has a high mortality rate, and early detection can greatly reduce patient mortality. Convolutional neural network (CNN) has been widely applied in the field of computer-aided diagnosis. To improve the ability of convolutional neural networks to accurately classify skin lesions, we propose a multiscale feature fusion model for skin lesion classification. We use a two-stream network, which are a densely connected network (DenseNet-121) and improved visual geometry group network (VGG-16). In the feature fusion module, we construct multireceptive fields to obtain multiscale pathological information and use generalized mean pooling (GeM pooling) to reduce the spatial dimensionality of lesion features. Finally, we built and tested a system with the developed skin lesion classification model. The experiments were performed on the dataset ISIC2018, which can achieve a good classification performance with a test accuracy of 91.24% and macroaverages of 95%.

Skin Lesion Classification on Imbalanced Data Using Deep Learning with Soft Attention

Today, the rapid development of industrial zones leads to an increased incidence of skin diseases because of polluted air. According to a report by the American Cancer Society, it is estimated that in 2022 there will be about 100,000 people suffering from skin cancer and more than 7600 of these people will not survive. In the context that doctors at provincial hospitals and health facilities are overloaded, doctors at lower levels lack experience, and having a tool to support doctors in the process of diagnosing skin diseases quickly and accurately is essential. Along with the strong development of artificial intelligence technologies, many solutions to support the diagnosis of skin diseases have been researched and developed. In this paper, a combination of one Deep Learning model (DenseNet, InceptionNet, ResNet, etc) with Soft-Attention, which unsupervisedly extract a heat map of main skin lesions. Furthermore, personal information including age and gender are also used. It is worth noting that a new loss function that takes into account the data imbalance is also proposed. Experimental results on data set HAM10000 show that using InceptionResNetV2 with Soft-Attention and the new loss function gives 90 percent accuracy, mean of precision, F1-score, recall, and AUC of 0.81, 0.81, 0.82, and 0.99, respectively. Besides, using MobileNetV3Large combined with Soft-Attention and the new loss function, even though the number of parameters is 11 times less and the number of hidden layers is 4 times less, it achieves an accuracy of 0.86 and 30 times faster diagnosis than InceptionResNetV2.

Deep Learning Based Semantic Image Segmentation Methods for Classification of Web Page Imagery

Semantic segmentation is the task of clustering together parts of an image that belong to the same object class. Semantic segmentation of webpages is important for inferring contextual information from the webpage. This study examines and compares deep learning methods for classifying webpages based on imagery that is obscured by semantic segmentation. Fully convolutional neural network architectures (UNet and FCN-8) with defined hyperparameters and loss functions are used to demonstrate how they can support an efficient method of this type of classification scenario in custom-prepared webpage imagery data that are labeled multi-class and semantically segmented masks using HTML elements such as paragraph text, images, logos, and menus. Using the proposed Seg-UNet model achieved the best accuracy of 95%. A comparison with various optimizer functions demonstrates the overall efficacy of the proposed semantic segmentation approach.

BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification

Early detection of brain tumors can save precious human life. This work presents a fully automated design to classify brain tumors. The proposed scheme employs optimal deep learning features for the classification of FLAIR, T1, T2, and T1CE tumors. Initially, we normalized the dataset to pass them to the ResNet101 pretrained model to perform transfer learning for our dataset. This approach results in fine-tuning the ResNet101 model for brain tumor classification. The problem with this approach is the generation of redundant features. These redundant features degrade accuracy and cause computational overhead. To tackle this problem, we find optimal features by utilizing differential evaluation and particle swarm optimization algorithms. The obtained optimal feature vectors are then serially fused to get a single-fused feature vector. PCA is applied to this fused vector to get the final optimized feature vector. This optimized feature vector is fed as input to various classifiers to classify tumors. Performance is analyzed at various stages. Performance results show that the proposed technique achieved a speedup of 25.5x in prediction time on the medium neural network with an accuracy of 94.4%. These results show significant improvement over the state-of-the-art techniques in terms of computational overhead by maintaining approximately the same accuracy.

LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification

Objective: The only possible solution to increase the patients' fatality rate is lung cancer early-stage detection. Recently, deep learning techniques became the most promising methods in medical image analysis compared with other numerous computer-aided diagnostic techniques. However, deep learning models always get lower performance when the model is overfitting. Methods: We present a Lung Cancer Data Augmented Ensemble (LCDAE) framework to solve the overfitting and lower performance problems in the lung cancer classification tasks. The LCDAE has 3 parts: The Lung Cancer Deep Convolutional GAN, which can synthesize images of lung cancer; A Data Augmented Ensemble model (DA-ENM), which ensembled 6 fine-tuned transfer learning models for training, testing, and validating on a lung cancer dataset; The third part is a Hybrid Data Augmentation (HDA) which combines all the data augmentation techniques in the LCDAE. Results: By comparing with existing state-of-the-art methods, the LCDAE obtains the best accuracy of 99.99%, the precision of 99.99%, and the F1-score of 99.99%. Conclusion: Our proposed LCDAE can overcome the overfitting issue for the lung cancer classification tasks by applying different data augmentation techniques, our method also has the best performance compared to state-of-the-art approaches.