A Skin Cancer Detection Interactive Application Based on CNN and NLP

Lung Cancer has long been regarded as one of the most threatened diseases to human beings, and detecting early malignant tumors is of vital importance for treatment. Contemporarily, Radiology departments in hospitals usually have to deal with multiple CT images to carry out the detection, which is a huge workload for doctors. Here, we propose a novel system to help with lung cancer detection. Specifically, deep feature based convolutional neural networks (CNN) is applied to classify lung cancer tumors, realizing an accuracy of 88%. Moreover, a chatbot based on natural language processing (NLP) technology is embedded into the system to provide immediate knowledge and information. These results shed light on how doctors' workload might be reduced to a considerable extent.

Ground short circuit faults in current transmission lines are common in the power systems. In order to prevent the power system from aggravating the accident caused by short-circuit faults of transmission lines, a novel convolutional neural network (CNN) model is constructed to identify the short-circuit fault of the transmission line in the power system. The CNN model is mainly consisted of five convolutional layers, three max-pooling layers, one concatenate layer, one dropout layer, one fully connected layer, and a Softmax classifier. This method uses a fixed time window to intercept system short-circuit fault data, extracts the deep features of these data from the training samples, and then corresponds the extracted features to labels one-to-one. Finally, in PSCAD/EMTDC, the new England 10 machine 39 nodes are taken as an example to realize the simulation. The experimental results show that the CNN model can quickly and accurately identify the short-circuit fault types, and the optimal model accuracy rate reaches 99.95%. The results of this manuscript -have positive effect on enhancing the disaster prevention capability and the operation stability of transmission lines.

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.

Sentiment analysis (SA) detects people’s opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer-based provide promising results for recognizing sentiment. Nonetheless, CNN has the advantage of extracting high-level features by using convolutional and max-pooling layers; it cannot efficiently learn a sequence of correlations. At the same time, Bidirectional RNN uses two RNN directions to improve extracting long-term dependencies. However, it cannot extract local features in parallel, and Transformer-based like Bidirectional Encoder Representations from Transformers (BERT) are the computational resources needed to fine-tune, facing an overfitting problem on small datasets. This paper proposes a novel attention-based model that utilizes CNNs with LSTM (named ACL-SA). First, it applies a preprocessor to enhance the data quality and employ term frequency-inverse document frequency (TF-IDF) feature weighting and pre-trained Glove word embedding approaches to extract meaningful information from textual data. In addition, it utilizes CNN’s max-pooling to extract contextual features and reduce feature dimensionality. Moreover, it uses an integrated bidirectional LSTM to capture long-term dependencies. Furthermore, it applies the attention mechanism at the CNN’s output layer to emphasize each word’s attention level. To avoid overfitting, the Guasiannoise and GuasianDroupout are adopted as regularization. The model’s robustness is evaluated on four English standard datasets, i.e., Sentiment140, US-airline, Sentiment140-MV, SA4A with various performance matrices, and compared efficiency with existing baseline models and approaches. The experiment results show that the proposed method significantly outperforms the state-of-the-art models.

Skin cancer is one of the most lethal types of cancer worldwide. There are different types of cancer but the most dangerous one is melanoma. In US alone, 1 out of 5 people will develop skin cancer by the age of 70 and more than 2 people die of skin cancer every hour. Taking these facts under consideration, early detection of skin cancer is crucial, and many lives could be saved. Deep learning technologies are proven to be of great aid in early detection. In this master thesis, different convolutional neural networks will be presented and tested on HAM10000 dataset of ISIC challenge dataset of 2018, containing 10.000 images of skin lesion from seven different classes. Neural networks will be trained on a portion of the dataset and then tested in new images. The convolutional networks used are Densenet201, Inception V3 and an Ensemble model of the previous two. Lastly, an application is developed which uses by default a convolutional network defined by the user. The user is able to feed an image in the application and a result out of the seven classes predefined will be presented.

SUMMARYThe interplay between the gut and skin microbiomes and their influence on skin cancer development has garnered significant attention. The gut and skin microbiomes, composed of bacteria, fungi, and viruses, play vital roles in immune modulation, inflammation regulation, and maintaining skin health. Dysbiosis in either microbiome may contribute to inflammatory skin conditions and cancer development through the gut-skin axis. The gut microbiota influences immune responses, skin barrier function, and carcinogenesis via microbial metabolites, such as short-chain fatty acids. These compounds impact systemic inflammation, immune cell activity, and response to immunotherapy, particularly in melanoma and non-melanoma skin cancers. Emerging evidence links distinct microbial profiles to skin cancer progression, with specific taxa associated with advanced stages. Conversely, certain skin commensals exhibit potential anti-tumor effects. In addition, microbial imbalances correlate with tumorigenesis via inflammatory and metabolic pathways. Advancements in microbiome profiling have further elucidated these associations, offering diagnostic and therapeutic opportunities. Potential interventions include probiotic therapies to restore microbial balance and enhance immunotherapy efficacy. However, the intricate dynamics of the gut-skin axis necessitate further investigation into causal mechanisms, microbial metabolite impacts, and personalized therapeutic strategies. This review highlights the dualistic role of the gut and skin microbiomes in skin cancer, emphasizing their diagnostic and therapeutic potential while proposing future research directions to unravel their complexities and clinical implications.

Skin cancer is a major issue regarding public health and mostly affects the Caucasians every year. It is the leading form of cancer affecting the people of USA. The mortality rate of skin cancer can be reduced to 2% if detected early. Dermatoscopy is a very popular tool in the diagnosis of benign as well as cancerous pigmented skin lesions. Dermatoscopy images are also suitable for automated diagnosis of pigmented skin lesions using deep learning techniques. This espouses the need of building an easily accessible, portable, faster and cheaper diagnostic system. The existing works mainly use pretrained network and huge processing resource. In this work, a 16-layer deep convolutional neural network is developed from the scratch. The network consists of 9 convolutional layers, 4 maxpool layers and 3 fully connected layers. The validation of the proposed architecture is done on the dermatoscopic images of the Kaggle dataset archive without augmentation. The proposed work offers an accuracy value of 87.58% which is much better than the existing works on the Kaggle dataset without augmentation. skin cancer, Dermatoscopic Images, convolutional neural network, Kaggle.

Incorporating AI in lung cancer management is a disruptive innovation that has improved diagnosis accuracy, prognosis prediction and treatment modalities. In this literature review, we seek to identify the role of artificial intelligence (AI) and machine learning (ML) in lung cancer detection, diagnosis and treatment between 2010 and 2023. A total of 55 studies were selected systematically from databases such as IEEE Xplore, Scopus and PubMed via a PRISMA-based approach. The analysis reveals that artificial intelligence (AI) techniques, specifically convolutional neural networks (CNNs) and natural language processing (NLP), highly improve the precision of initial detection and imaging of lung cancer. Also, CNN distinguishes between benign and malignant nodules, thus aiding early diagnosis and reducing unnecessary biopsies. On the other hand, NLP is utilized to extract relevant clinical information from electronic health records and unstructured medical texts, thereby enhancing the understanding of patient histories and improving treatment planning. Sensitive and specific scores usually higher than standard techniques characterize these technologies. Results show that traditional statistical approaches couldn’t match AI models whose predictive accuracies are outstanding while providing better care to patients through personalised treatment plans. Furthermore, multi-omics data analysis for personalised treatment planning and clinical decision-making optimisation via Clinical Decision Support Systems (CDSS) powered by AI are some ways artificial intelligence has exhibited its potential in this area. Given this, future studies should aim to fine-tune AI algorithms, improve data integration, and address ethical issues promoting responsible use of AI technologies in clinical practice settings. Despite these advances, data quality, model interpretability, and integration into clinical workflows persist. This review demonstrates the demand for continued research and collaboration from different disciplines so that the complete possibilities of AI in fighting lung cancer may be realized.

Skin cancer, abnormal skin cell development, is a common and fatal type of cancer that occurs when skin is exposed to sunlight. Early diagnosis is important to prevent more serious consequences. Implementing a detection system would save more time for doctors and give patients efficient and low-cost diagnoses. In this paper, we built a skin cancer classification system based on Convoluted Neural Network (CNN) for seven majority skin cancers, and Natural Language Processing (NLP), for interaction with a human. We also implemented self-defined CNN, LeNet5, AlexNet, ResNet, VGG-16 in our system to compare their accuracy and discover reasons behind those output data. Finally, our self-defined CNN gets 0.8237 testing accuracy after training, LeNet5 results in 0.4857 testing accuracy, AlexNet produces 0.4715 testing accuracy, ResNet yields 0.8995 testing accuracy, and VGG-16 shown 0.7544 testing accuracy. The result indicates that ResNet-18 performs best through all models.

In order to overcome the effects of posture, illumination, expression and other factors on face recognition, this paper proposes an algorithm which is based on local binary pattern (LBP) and convolutional neural network (CNN). LBP is a texture description method which describes the local texture features of an image. It has good robustness of illumination and posture. CNN can effectively extract the spatial features of images and reduce the dimensions of features. This paper combines the advantages of LBP and CNN to improve the accuracy of face recognition. The CNN in this algorithm has four convolution layers, two max-pooling layers, one activation layer, one fully connected layer, and one output layer. In order to optimize the network structure, batch normalization layer is added after the convolution layer. We get the local binary pattern coded images and put the images as the input of the CNN and train the network. Hence, we can use the well-trained CNN for classification and identification. The Experiments on the CMU-PIE face database show that our algorithm can effectively improve the rate of face recognition.

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.

Deep learning models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models have recently emerged as effective solutions to various NLP tasks with comparatively remarkable results. The CNN model efficiently extracts higher level features using convolutional layers and max-pooling layers while the LSTM model allows capturing long-term dependencies between word sequences. In this paper, we propose a hybrid CNN-LSTM model taking advantage of both models to overcome the sentiment analysis problem on Twitter data. We employ a multi-channel CNN that extracts local n-gram features in different sizes using several filters of different lengths. We also employ a weighted average word embeddings method which incorporates sentiment information in the continuous representation of words based on an adapted version of the delta TFIDF measure. These word representations will be the input for the CNN-LSTM model. Experiment results substantiate our intuition by reaching good macro average recall and accuracy scores beating several existing models as well as our same model using individual CNN and LSTM.

Natural language processing (NLP) technology is an important constituent of artificial intelligence, focusing on the interaction between computers and human natural language, with the aim of enabling computers to understand, analyze, generate and process human languages. The fields of legal aid and judicial practice are also strongly related to texts, therefore, natural language processing technology has shown great potential in the legal field nowadays. In this study, the author reviews the development status, core technologies, application areas, and application value of natural language processing technology in the fields of legal aid and judicial practice. In addition, the study summarizes the difficulties and challenges faced by natural language processing technology in the field of legal aid and judicial practice. In particular, the current legal natural language processing technology still has technical problems, such as difficulty in processing complex and long legal texts, a lack of certain logical reasoning ability, insufficient understanding of non-professional questions, and a lack of public datasets, which leads to high model training costs, etc. In response to these problems, the study points out the need to make natural language processing models more adaptable to legal language, and proposes suggestions such as enhancing the popularity of models and open datasets.

The World Banking with its various financial services is an easy target for fraudsters to carry out their actions. Various kinds of fraud that occurred such as credit card fraud, online payment fraud, debit card fraud, online transaction fraud, e-commerce fraud and other services including interbank online transactions. Fast and reliable fraud detection is important because many financial losses have occurred due to fraud. The objective of this study is obtaining a more effective deep learning model for fraud detection in the interbank online transaction system compared to similar models. This study using CNN, LSTM and hybrid model CNN-LSTM models are used to build an interbank online transaction system. The proposed model CNN consist of three convolution layer, one maxpooling layer, one dropout layer and one fully connected layer. The proposed model LSTM built by double layer LSTM with each layer consist 32 cell LSTM, dropout layer and one fully connected layer. The proposed model CNN-LSTM built by combination three convolution layer, 1 maxpooling layer, dropout layer, 1 LSTM layer with 64 LSTM cell and one fully connected layer. The Dataset taken from an interbank online transaction in March 2021 from one of the switching company in Indonesia. SMOTE is use to overcome the imbalance Dataset in training and validation Dataset. The Dataset contains 279513 transactions with 2374 transactions categorized as fraud. The results showed that the CNN model scored an F1-score value at 93,09%, followed by the LSTM model at 86,25% and the CNN-LSTM hybrid model at 69,22%. Based on these results, the proposed CNN model can be accurate for fraud detection in interbank online transaction systems compared to similar models.

The incidence of skin cancer is increasing at an alarming rate. To discuss current epidemiologic data concerning the incidence, morbidity, environmental influences, predisposing, host conditions, precursor lesions, and prevention of melanoma and nonmelanoma (basal and squamous cell) skin cancer. The current literature was reviewed in order to provide current epidemiologic data for melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Skin cancer is exceedingly common and the incidence is rising rapidly. Although the mortality rate for nonmelanoma skin cancer (NMSC) is decreasing, that of melanoma is increasing. Both NMSC and melanoma are associated with significant morbidity. Whereas chronic sun exposure is the main cause of NMSC, the development of melanoma appears to be related to intense, intermittent sun exposure. Ozone depletion has contributed to rising incidence rates of both NMSC and melanoma. In contrast to NMSC, there is not a direct relationship between ultraviolet radiation and melanoma. Genetic susceptibility significantly increases the lifetime risk of acquiring melanoma. There is no precursor lesion for BCC. Precursor lesions for invasive SCC include actinic keratoses and SCC in situ. Melanoma may arise from benign nevi and dysplastic nevi. Prevention of melanoma and NMSC is extremely important since prognosis improves with early detection. Prevention may be achieved by educating patients and physicians how to detect skin cancers early and by decreasing or eliminating exposure to ultraviolet light. The incidence of skin cancer has reached epidemic proportions. Only through heroic efforts by health care professionals and the general public to prevent the development or progression of skin cancer will this epidemic be abated.