Racial Identity-Aware Facial Expression Recognition Using Deep Convolutional Neural Networks

An efficient leukemia prediction method using machine learning and deep learning with selected features

Leukemia is a serious problem affecting both children and adults, leading to death if left untreated. Leukemia is a kind of blood cancer described by the rapid proliferation of abnormal blood cells. An early, trustworthy, and precise identification of leukemia is important to treating and saving patients' lives. Acute and myelogenous lymphocytic, chronic and myelogenous leukemia are the four kinds of leukemia. Manual inspection of microscopic images is frequently used to identify these malignant growth cells. Leukemia symptoms include fatigue, a lack of enthusiasm, a dull appearance, recurring illnesses, and easy blood loss. Identifying subtypes of leukemia for specialized therapy is one of the hurdles in this area. The suggested work predicts and classifies leukemia subtypes in gene data CuMiDa (GSE9476) using feature selection and ML techniques. The Curated Microarray Database (CuMiDa) collected 64 samples representing five classes of leukemia genes out of 22283 genes. The proposed approach utilizes the 25 most differentiating selected features for classification using machine and deep learning techniques. This study has a classification accuracy of 96.15% using Random Fores, 92.30 using Linear Regression, 96.15% using SVM, and 100% using LSTM. Deep learning methods have been shown to outperform traditional methods in leukemia gene classification by utilizing specific features.

Advanced digital image forensics: A hybrid framework for copy-move forgery detection in multimedia security

Particularly in validating image integrity, advances in digital image analysis have profoundly affected forensic investigation. The growing reliance on digital image technology can be attributed in part to the broad availability of consistent and effective image-capturing technologies. The simplicity of changing image content thanks to advanced image-editing technologies presents fresh difficulties for forensic analysis. A structured hybrid framework is presented for finding important objects in images. It does this by using fast Fourier transformation (FFT) for frequency domain filtering, scale-invariant feature transformation (SIFT), and oriented FAST and rotated BRIEF (ORB) to pull out key points. The MobilenetV2 and VGG16 models extract features from key point areas to detect copy-move forgery. After that, an attention mechanism combines and normalizes these aspects. Key point matching uses the Euclidean distance; DBSCAN clustering groups pertinent key points for object localization. The suggested approach shows better performance than current methods and detects image copy-move forgery rather successfully. The framework's robustness is verified against image blurring, contrast alteration, color reduction, image compression, and brightness change among other post-processing techniques. Since photographs are altered, traditional approaches can struggle with a lot of variety; however, the proposed method combines advanced deep learning models and clustering techniques to make detection more accurate. Extensive testing on five benchmark copy-move forgeries datasets reveals that the suggested strategy may beat present techniques. This work offers a sophisticated automated approach to guarantee digital image integrity and identify image manipulation.

Impact of fine-tuning parameters of convolutional neural network for skin cancer detection

Melanoma skin cancer is a deadly disease with a high mortality rate. A prompt diagnosis can aid in the treatment of the disease and potentially save the patient's life. Artificial intelligence methods can help diagnose cancer at a rapid speed. The literature has employed numerous Machine Learning (ML) and Deep Learning (DL) algorithms to detect skin cancer. ML algorithms perform well for small datasets but cannot comprehend larger ones. Conversely, DL algorithms exhibit strong performance on large datasets but misclassify when applied to smaller ones. We conduct extensive experiments using a convolutional neural network (CNN), varying its parameter values to determine which set of values yields the best performance measure. We discovered that adding layers, making each Conv2D layer have multiple filters, and getting rid of dropout layers greatly improves the accuracy of the classifiers, going from 62.5% to 85%. We have also discussed the parameters that have the potential to significantly impact the model's performance. This shows how powerful it is to fine-tune the parameters of a CNN-based model. These findings can assist researchers in fine-tuning their CNN-based models for use with skin cancer image datasets.

Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks

Autism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre-trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state-of-the-art models in terms of accuracy and computational cost.

Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models

One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer.

Skin Cancer Disease Detection Using Transfer Learning Technique

Melanoma is a fatal type of skin cancer; the fury spread results in a high fatality rate when the malignancy is not treated at an initial stage. The patients’ lives can be saved by accurately detecting skin cancer at an initial stage. A quick and precise diagnosis might help increase the patient’s survival rate. It necessitates the development of a computer-assisted diagnostic support system. This research proposes a novel deep transfer learning model for melanoma classification using MobileNetV2. The MobileNetV2 is a deep convolutional neural network that classifies the sample skin lesions as malignant or benign. The performance of the proposed deep learning model is evaluated using the ISIC 2020 dataset. The dataset contains less than 2% malignant samples, raising the class imbalance. Various data augmentation techniques were applied to tackle the class imbalance issue and add diversity to the dataset. The experimental results demonstrate that the proposed deep learning technique outperforms state-of-the-art deep learning techniques in terms of accuracy and computational cost.