A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.

There are numerous pre-trained Convolutional Neural Networks (CNN) introduced in the literature, such as AlexNet, VGG-19, and ResNet. These pre-trained CNN models could be reused and applied to tackle different image recognition problems. Unfortunately, these pre-trained CNN models are complex and have a large number of convolutional filters. To tackle such a complexity challenge, this research aims to evolve a pre-trained VGG-19 using an efficient two-layers optimizer. The proposed optimizer performs filters selection of the last layers of VGG-19 guided by the accuracy of the linear SVM classifier. The proposed approach has three main advantages. Firstly, it adopts a powerful two-layers optimizer that works with a micro swarm population. Secondly, it automatically evolves a lightweight deep model which uses a small number of VGG-19 convolutional filters. Thirdly, It applies the developed model for real-world road damage detection from drone-based images. To evaluate the effectiveness of the proposed approach, a total of 529 images were captured by using a drone-based camera for various road damages. Reported results indicated that the proposed model achieved 96.4% F1-score accuracy with a reduction of VGG-19 filter up to 52%. In addition, the proposed two-layers optimizer was able to outperform several related optimizers such as AOA (Arithmetic Optimization Algorithm), Wild Geese Algorithm (WGO), Particle Swarm Optimization (PSO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).

Cancer of the brain is most common in the elderly and young and can be fatal in both. Brain tumours can heal better if they are diagnosed and treated quickly. When it comes to processing medical images, the deep learning method is essential in aiding humans in diagnosing various diseases. Classifying brain tumours is an essential step that relies heavily on the doctor’s experience and training. A smart system for detecting and classifying these tumours is essential to aid in the non-invasive diagnosis of brain tumours using MRI (magnetic resonance imaging) images. This work presents a novel hybrid deep learning CNN-based structure to distinguish between three distinct types of human brain tumours through MRI scans. This paper proposes a method that employs a dual approach to classification using deep learning and CNN. The first approach combines the unsupervised classification of an SVM for pattern classification with a pre-trained CNN (i.e., SqueezeNet) for feature extraction. The second approach combines the supervised soft-max classifier with a finely tuned SqueezeNet. To evaluate the efficacy of the suggested method, MRI scans of the brain were used to analyse a total of 1937 images of glioma tumours, 926 images of meningioma tumours, 926 images of pituitary tumours, and 396 images of a normal brain. According to the experiment results, the finely tuned SqueezeNet model obtained an accuracy of 96.5%. However, when SqueezeNet was used as a feature extractor and an SVM classifier was applied, recognition accuracy increased to 98.7%.

Rumex obtusifolius Linnaeus (R. obtu. L.) is one of the vital broad-leaved weeds in grassland that needs removal. It affects dairy products and reduces their quality. Hand-removal methods are costly and time-consuming. Chemical treatment using herbicides has a negative impact on crops and causes environmental pollution. In smart farming, weeding is performed by using computer vision to recognize the weeds efficiently and effectively. Conventional machine learning (ML)-based algorithms face challenges, especially in identifying the weeds in real-world data due to a lack of features. Deep learning (DL) approaches use self-learning to extract all potential features that assist in classifying malignant weed species accurately. Recently, single deep learning methods achieved high performance in identifying well-separated and illumination but suffered from misclassification in more sophisticated cases such as overlapping and partial occlusion leaves. This paper presents a hybrid Convolutional Neural Network (CNN) model of three state-of-the-art CNNs to classify Rumex obtusifolius. The proposed model utilizes convolutional neural networks to extract features and classify images. The framework of the proposed method comprises three paramount stages to accomplish the classification key idea, including the data preparation phase, pre-processing phase, and classification phase. A hybrid model of three CNN extractor networks is used as the backbone in the classification stage. Our tested data is real-world data that includes multi-circumstances (overlap, occlusion, various illuminations, etc.) acquired from nature. The first extractor is the Visual Graphics Group-16 (VGG-16) for well-separated leaves and non-complicated issues. The second extractor is Residential Energy Services Network-50 (ResNet-50), to overcome complex real-world issues. The third extractor is Inception-v3 to solve the illumination problem. Therefore, combining three networks into one model improves the discriminatory ability to extract additional useful features. The proposed model has been tested using two benchmark datasets for Rumex weed plants. Both of these datasets were captured in real-world environments. The first dataset consists of 900 samples, while the second dataset consists of 677 samples. Each dataset is individually tested in our proposed model to evaluate the classification accuracy using a set of standard evaluation metrics including accuracy, precession, recall, True-Positive Rate (TPR), False-Positive Rate (FPR), and F1-score. The total averages of the proposed model on both datasets are 97.51%, 97.4%, 94.45%, and 95.9% on the accuracy, recall, precision, and F1-score, respectively.

Leukemia is a form of cancer that affects the bone marrow and lymphatic system, and it requires complex treatment strategies that vary with each subtype. Due to the subtle morphological differences among these types, monitoring gene expressions is crucial for accurate classification. Manual or pathological testing can be time-consuming and expensive. Therefore, data-driven methods and machine learning algorithms offer an efficient alternative for leukemia classification. This study introduced a novel super learning model that leverages heterogeneous machine learning models to analyze gene expression data and classify leukemia cells. The proposed approach incorporates an entropy-based feature importance technique to identify the gene profiles most significant to the labeling process. The strength of this super learning model lies in its final super learner, Random Forest, which effectively classifies cross-validated data from the candidate learners. Validation on a gene expression monitoring dataset demonstrates that this model outperforms other state-of-the-art models in predictive accuracy. The study contributes to the knowledge regarding the use of advanced machine learning techniques to improve the accuracy and reliability of leukemia classification using gene expression data, addressing the challenges of traditional methods that rely on clinical features and morphological examination.

Sketch-based image retrieval (SBIR) utilizes sketches to search for images containing similar objects or scenes. Due to the proliferation of touch-screen devices, sketching has become more accessible and therefore has received increasing attention. Deep learning has emerged as a potential tool for SBIR, allowing models to automatically extract image features and learn from large amounts of data. To the best of our knowledge, there is currently no systematic literature review (SLR) of SBIR with deep learning. Therefore, the aim of this review is to incorporate related works into a systematic study, highlighting the main contributions of individual researchers over the years, with a focus on past, present and future trends. To achieve the purpose of this study, 90 studies from 2016 to June 2023 in 4 databases were collected and analyzed using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework. The specific models, datasets, evaluation metrics, and applications of deep learning in SBIR are discussed in detail. This study found that Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) are the most widely used deep learning methods for SBIR. A commonly used dataset is Sketchy, especially in the latest Zero-shot sketch-based image retrieval (ZS-SBIR) task. The results show that Mean Average Precision (mAP) is the most commonly used metric for quantitative evaluation of SBIR. Finally, we provide some future directions and guidance for researchers based on the results of this review.