Lightweight Convolutional Neural Network Architecture Research Articles

Potato Leaf Disease Detection Based on a Lightweight Deep Learning Model

Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and efficient solutions. The management of potato diseases is critical to the agricultural industry, as these diseases can lead to substantial losses in crop production. The prompt identification and classification of potato leaf diseases are essential to mitigating such losses. In this paper, we present a novel approach that integrates a lightweight convolutional neural network architecture, RegNetY-400MF, with transfer learning techniques to accurately identify seven different types of potato leaf diseases. The proposed method not only enhances the precision of potato leaf disease detection but also reduces the computational and storage demands, with a mere 0.40 GFLOPs and a model size of 16.8 MB. This makes it well-suited for use on edge devices with limited resources, enabling real-time disease detection in agricultural environments. The experimental results demonstrated that the accuracy of the proposed method in identifying seven potato leaf diseases was 90.68%, providing a comprehensive solution for potato crop management.

Real-time field disease identification based on a lightweight model

In the field of agricultural technology, rapid and accurate diagnosis of crop diseases is crucial for ensuring crop health and yield. However, most methods for automatic diagnosis of plant diseases are primarily based on high-computational-performance server platforms, which limits their application in real-world environments. This paper establishes a crop disease dataset collected from real field scenarios, which is utilized for training, validating the proposed models, and enhancing model generalization in existing research. It provides an accurate and practical database for future research endeavors. The key innovation of this study lies in the development of a lightweight convolutional neural network architecture with high real-time capabilities, characterized by its high precision and efficiency. The network abandons traditional deep convolution, adopting partial convolution and point-wise convolution techniques instead. This approach significantly reduces computational complexity while maintaining high accuracy, making the model highly suitable for application in resource-constrained real-time environments. The proposed method achieved an accuracy of 99.04 % on the open-source PlantVillage dataset and 92.82 % accuracy on a dataset with complex backgrounds constructed. Additionally, this paper also investigates the deployment of the proposed method and other mature technologies on mobile platforms. The focus of this evaluation is to compare the real-time performance on devices with limited computational resources, thereby verifying the model’s applicability in the real world.

ShuffleNeMt: modern lightweight convolutional neural network architecture

ShuffleNeMt: modern lightweight convolutional neural network architecture

Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis

BackgroundIdentification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment.MethodsUsing a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification.ResultsThe lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR).ConclusionsThese results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.

Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM

Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security.

Efficient and lightweight convolutional neural network architecture search methods for object classification

Determining the architecture of deep learning models is a complex task. Several automated search techniques have been proposed, but these methods typically require high-performance graphics processing units (GPUs), manual parameter adjustments, and specific training approaches. This study introduces an efficient, lightweight convolutional neural network architecture search approach tailored for object classification. It features an optimized search space design and a novel controller design.This study introduces a refined search space design incorporating optimizations in both spatial and operational aspects. The focus is on the synergistic integration of convolutional units, dimension reduction units, and the stacking of Convolutional Neural Network (CNN) architectures. To enhance the search space, ShuffleNet modules are integrated, reducing the number of parameters and training time. Additionally, BlurPool is implemented in the dimension reduction unit operation to achieve translational invariance, alleviate the gradient vanishing problem, and optimize unit compositions. Moreover, an innovative controller model, Stage LSTM, is proposed based on Long Short-Term Memory (LSTM) to generate lightweight architectural sequences. In conclusion, the refined search space design and the Stage LSTM controller model are synergistically combined to establish an efficient and lightweight architecture search technique termed Stage and Lightweight Network Architecture Search (SLNAS).The experimental results highlight the superior performance of the optimized search space design, primarily when implemented with the Stage LSTM controller model. This approach shows significantly improved accuracy and stability compared to random, traditional LSTM, and Genetic Algorithm (GA) controller models, with statistically significant differences. Notably, the Stage LSTM controller excels in accuracy while producing models with fewer parameters within the expanded architecture search space.The study adopts the Stage LSTM controller model due to its ability to approximate optimal sequence structures, particularly when combined with the optimized search space design, referred to as SLNAS. SLNAS's performance is evaluated through experiments and comparisons with other Neural Architecture Search (NAS) and object classification methods from different researchers. These experiments consider model parameters, hardware resources, model stability, and multiple datasets. The results show that SLNAS achieves a low error rate of 2.86 % on the CIFAR-10 dataset after just 0.2 days of architecture search, matching the performance of manually designed models but using only 2 % of the parameters. SLNAS consistently demonstrates robust performance across various image classification domains, with an approximate parameter count 700,000.To summarize, SLNAS emerges as a highly effective automated network architecture search method tailored for image classification. It streamlines the model design process, making it accessible to researchers without specialized knowledge in deep learning. Optimizing this method unlocks the full potential of deep learning across diverse research areas. Interested parties can publicly access the source code and pre-trained models through the following link: https://github.com/huanyu-chen/LNASG-and-SLNAS-model.

Maize plant growth period identification based on MobileNet and design of growth control system

To address the current inefficiencies and subjective nature of manual observation in maize cultivation, with the aim of achieving high efficiency and productivity, this study focused on the DeMaya D3 maize variety. It proposes a maize growth stage recognition method based on the MobileNet model, which is a lightweight convolutional neural network architecture. The method was tested and achieved recognition accuracies of 0.98, 0.96, 0.92, 0.85, and 0.97 for different growth stages, respectively. Additionally, a maize growth prediction model was developed. Based on data collected from experimental plots regarding maize plant height and stem diameter, the Prophet model and an optimized version of the Prophet model were used to forecast maize growth trends. The Prophet model is an open-source tool for time series forecasting. Comparative analysis was conducted between the predictions of the original Prophet model and the optimized version. The relative errors of the Prophet model predictions were 0.85%, 2.11%, and 0.79%, while those of the optimized Prophet model were 0.76%, 0.47%, and 0.71%. Compared to the Prophet model, the optimized model reduced errors by 0.09%, 1.64%, and 0.08%, respectively. The maize plant growth control system was designed to obtain the information through the collection layer. The decision-making layer judged the soil nutrient absorption and growth status. Finally, the management layer controlled water and fertilizer.

EfficientSkinSegNet: a lightweight convolutional neural network for accurate skin lesion segmentation

Accurate segmentation of skin lesions is crucial for the early detection and treatment of skin cancer. In this study, we propose EfficientSkinSegNet, a novel lightweight convolutional neural network architecture specifically designed for precise skin lesion segmentation. EfficientSkinSegNet incorporates efficient feature extraction encoders and decoders, leveraging multi-head convolutional attention and spatial channel attention mechanisms to extract and enhance informative features while eliminating redundant ones. Furthermore, a multi-scale feature fusion module is introduced in the skip connections to facilitate effective fusion of features at different scales. Experimental evaluations on benchmark datasets demonstrate that EfficientSkinSegNet outperforms state-of-the-art methods in terms of segmentation accuracy while maintaining a compact model size. The proposed network shows promise for practical clinical diagnostic applications, providing a balance between segmentation performance and computational efficiency. Future research will focus on evaluating EfficientSkinSegNet’s performance on diverse semantic segmentation tasks and optimizing it for medical image analysis.

Almond (Prunus dulcis) varieties classification with genetic designed lightweight CNN architecture

Almond (Prunus dulcis) is a nutritious food with a rich content. In addition to consuming as food, it is also used for various purposes in sectors such as medicine, cosmetics and bioenergy. With all these usages, almond has become a globally demanded product. Accurately determining almond variety is crucial for quality assessment and market value. Convolutional Neural Network (CNN) has a great performance in image classification. In this study, a public dataset containing images of four different almond varieties was created. Five well-known and light-weight CNN models (DenseNet121, EfficientNetB0, MobileNet, MobileNet V2, NASNetMobile) were used to classify almond images. Additionally, a model called 'Genetic CNN', which has its hyperparameters determined by Genetic Algorithm, was proposed. Among the well-known and light-weight CNN models, NASNetMobile achieved the most successful result with an accuracy rate of 99.20%, precision of 99.21%, recall of 99.20% and f1-score of 99.19%. Genetic CNN outperformed well-known models with an accuracy rate of 99.55%, precision of 99.56%, recall of 99.55% and f1-score of 99.55%. Furthermore, the Genetic CNN model has a relatively small size and low test time in comparison to other models, with a parameter count of only 1.1 million. Genetic CNN is suitable for embedded and mobile systems and can be used in real-life solutions.

A Transfer Learning Approach for Facial Emotion Recognition Using a Deep Learning Model

The facial expression recognition (FER) system is the process of identifying the emotional state of a person. Emotion recognition from facial expressions is a rapidly growing area of research with numerous applications, including psychology, marketing, and human-computer interaction. This paper presents a novel approach to FER employing deep learning techniques, specifically leveraging the power of transfer learning methodology. To address the challenge of accurately recognizing different emotions, including angry, disgusted, afraid, happy, sad, surprised, and neutral, from facial expressions, a pre-trained model MobileNetV2 architecture has been fine-tuned for improving accuracy. It is a lightweight convolutional neural network architecture, specifically designed for efficient on-device inference. For evaluating the performance of the proposed FER model, the state-of-the-art FER-2013 dataset, along random images and video clips has been employed. Experimental results demonstrate that the proposed FER model attained a remarkable accuracy rate exceeding 99% when tested on diverse sets of random images and video clips. Moreover, the system achieved a notable accuracy of 61% when evaluated against the FER-2013 dataset. Overall, this approach presents a significant advancement in the field of real-time facial expression recognition using the MobileNetV2 architecture with FER2013 dataset, which may improve the quality of human-computer interactions.

Self-attention transformer unit-based deep learning framework for skin lesions classification in smart healthcare

Rising mortality rates in recent years have elevated melanoma to the ranks of the world’s most lethal cancers. Dermoscopy images (DIs) have been used in smart healthcare applications to determine medical features using deep transfer learning (DTL). DI-related lesions are widespread, have local features, and are associated with uncertainty. There are three components to our bi-branch parallel model: (1) the Transformer module (TM), (2) the self-attention unit (SAU), and (3) a convolutional neural network (CNN). With CNN and TM able to extract local and global features, respectively, a novel model has been developed to fuse global and local features using cross-fusion to generate fine-grained features. Parallel systems between the branches are merged using a feature-fusion architecture, resulting in a pattern that identifies the characteristics of a variety of lesions. Moreover, this paper proposes an optimized and lightweight CNN architecture version (optResNet-18) that discriminates skin cancer lesions with high accuracy. To verify the proposed method, the procedure evaluated the accuracy for the ISIC-2019 and the PH2 datasets as 97.48 and 96.87%, respectively, a significant difference over traditional CNN networks (e.g., ResNet-50 and ResNet-101) and the TM. The proposed model outperforms state-of-the-art performance metrics such as AUC, F1-score, specificity, precision, and recall. The proposed method can also be used as a generalizable model to diagnose different lesions in DIs with smart healthcare applications by combining DTL and medical imaging. With the proposed e-Health platform, skin diseases can be detected in real-time, which is crucial to speedy and reliable diagnostics.

A Comparative Study of MobileNet Architecture Optimizer for Crowd Prediction

Artificial intelligence technology has grown quickly in recent years. Convolutional neural network (CNN) technology has also been developed as a result of these developments. However, because convolutional neural networks entail several calculations and the optimization of numerous matrices, their application necessitates the utilization of appropriate technology, such as GPUs or other accelerators. Applying transfer learning techniques is one way to get around this resource barrier. MobileNetV2 is an example of a lightweight convolutional neural network architecture that is appropriate for transfer learning. The objective of the research is to compare the performance of SGD and Adam using the MobileNetv2 convolutional neural network architecture. Model training uses a learning rate of 0.0001, batch size of 32, and binary cross-entropy as the loss function. The training process is carried out for 100 epochs with the application of early stop and patience for 10 epochs. Result of this research is both models using Adam's optimizer and SGD show good capability in crowd classification. However, the model with the SGD optimizer has a slightly superior performance even with less accuracy than model with Adam optimizer. Which is model with Adam has accuracy 96%, while the model with SGD has 95% accuracy. This is because in the graphical results model with the SGD optimizer shows better stability than the model with the Adam optimizer. The loss graph and accuracy graph of the SGD model are more consistent and tend to experience lower fluctuations than the Adam model.

An extremely lightweight CNN model for the diagnosis of chest radiographs in resource-constrained environments.

In recent years, deep learning methods have been successfully used for chest x-ray diagnosis. However, such deep learning models often contain millions of trainable parameters and have high computation demands. As a result, providing the benefits of cutting-edge deep learning technology to areas with low computational resources would not be easy. Computationally lightweight deep learning models may potentially alleviate this problem. We aim to create a computationally lightweight model for the diagnosis of chest radiographs. Our model has only 0.14M parameters and 550 KB size. These make the proposed model potentially useful for deployment in resource-constrainedenvironments. We fuse the concept of depthwise convolutions with squeeze and expand blocks to design the proposed architecture. The basic building block of our model is called Depthwise Convolution In Squeeze and Expand (DCISE) block. Using these DCISE blocks, we design an extremely lightweight convolutional neural network model (ExLNet), a computationally lightweight convolutional neural network (CNN) model for chest x-raydiagnosis. We perform rigorous experiments on three publicly available datasets, namely, National Institutes of Health (NIH), VinBig ,and Chexpert for binary and multi-class classification tasks. We train the proposed architecture on NIH dataset and evaluate the performance on VinBig and Chexpert datasets. The proposed method outperforms several state-of-the-art approaches for both binary and multi-class classification tasks despite having a significantly less number ofparameters. We design a lightweight CNN architecture for the chest x-ray classification task by introducing ExLNet which uses a novel DCISE blocks to reduce the computational burden. We show the effectiveness of the proposed architecture through various experiments performed on publicly available datasets. The proposed architecture shows consistent performance in binary as well as multi-class classification tasks and outperforms other lightweight CNN architectures. Due to a significant reduction in the computational requirements, our method can be useful for resource-constrained clinical environment aswell.

Driver Behaviour Detection Based on Convolution Neural System

Abstract: Driving is a set of behaviours that need high concentration. Sometimes these behaviours are dominated by other acts such as smoking, eating, drinking, talking, phone calls, adjusting the radio, or drowsiness. These are also the main causes of current traffic accidents. Therefore, developing applications to warn drivers in advance is essential. This research introduces a light-weight convolutional neural network architecture to recognize driver behaviours, helping the warning system to provide accurate information and to minimize traffic collisions. This network is a combination of feature extraction and classifier modules. The feature extraction module uses the advantages of the standard convolution layers, depth wise separable convolution layers, average pooling layers, and proposed adaptive connections to extract the feature maps. The benefit of the convolution block attention module is deployed in the feature extraction module that guides the network in learning the salient features. The classifier module is comprised of a global average pooling and soft max layer to calculate the probability of each class. The overall design optimizes the network parameters and maintains classification accuracy. The entire network is trained and evaluated on three benchmark datasets.

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts. Chest X-ray (CXR) images of lung diseases were collected from several publicly available databases. A lightweight convolutional neural network (CNN) has been used to extract characteristic features from the raw pixel values of the CXR images. The best feature subset has been identified using the Pearson Correlation Coefficient (PCC). Finally, the extreme learning machine (ELM) has been used to perform the classification task to assist faster learning and reduced computational complexity. The proposed CNN-PCC-ELM model achieved an accuracy of 96.22% with an Area Under Curve (AUC) of 99.48% for eight class classification. The outcomes from the proposed model demonstrated better performance than the existing state-of-the-art (SOTA) models in the case of COVID-19, pneumonia, and tuberculosis detection in both binary and multiclass classifications. For eight class classification, the proposed model achieved precision, recall and fi-score and ROC are 100%, 99%, 100% and 99.99% respectively for COVID-19 detection demonstrating its robustness. Therefore, the proposed model has overshadowed the existing pioneering models to accurately differentiate COVID-19 from the other lung diseases that can assist the medical physicians in treating the patient effectively.

Lightweight convolutional neural network architecture implementation using TensorFlow lite

Lightweight convolutional neural network architecture implementation using TensorFlow lite

Lite-SeqCNN: A Light-Weight Deep CNN Architecture for Protein Function Prediction.

The short-and-long range interactions amongst amino-acids in a protein sequence are primarily responsible for the function performed by the protein. Recently convolutional neural network (CNN)s have produced promising results on sequential data including those of NLP tasks and protein sequences. However, CNN's strength primarily lies at capturing short range interactions and are not so good at long range interactions. On the other hand, dilated CNNs are good at capturing both short-and-long range interactions because of varied - short-and-long - receptive fields. Further, CNNs are quite light-weight in terms of trainable parameters, whereas most existing deep learning solutions for protein function prediction (PFP) are based on multi-modality and are rather complex and heavily parametrized. In this paper, we propose a (sub-sequence + dilated-CNNs)-based simple, light-weight and sequence-only PFP framework Lite-SeqCNN. By varying dilation-rates, Lite-SeqCNN efficiently captures both short-and-long range interactions and has (0.50-0.75 times) fewer trainable parameters than its contemporary deep learning models. Further, Lite-SeqCNN + is an ensemble of three Lite-SeqCNNs developed with different segment-sizes that produces even better results compared to the individual models. The proposed architecture produced improvements upto 5% over state-of-the-art approaches Global-ProtEnc Plus, DeepGOPlus, and GOLabeler on three different prominent datasets curated from the UniProt database.

Segmentation and Classification of Dermatoscopic Skin Lesion images using U-Net and MobileNet models

The 17th most prevalent cancer in the world is cutaneous melanoma. Early detection and adequate treatment are essential for skin cancer success. It might not be possible to tell benign lesions from malignant tumours just by looking at them. The histopathological study of the skin biopsy is the gold standard procedure. Skin biopsy has some drawbacks, including its invasiveness, the pain it causes, and the requirement for many samples for suspected lesions with multiple presentations. Clinical diagnosis can also be aided by noninvasive tools. Several non-invasive imaging techniques are now available to diagnose melanoma because of numerous scientific and technological developments. The most advanced network for pattern identification in medical image analysis is the convolutional neural network (CNN). Thus, utilizing these advanced techniques a Skin Lesion Classifier can be built, which performs segmentation of the Lesion area in the pre-process step to avoid extracting and remembering other additional features from the background of the dermatoscopic image. For the segmentation task U-Net Architecture is utilized on the PH2 dataset and obtained validation accuracy of 95%. And performs well on the test data. The segmented images are then used to train a lightweight CNN Architecture “MobileNet” using some pretrained weights from imagenet dataset and produced validation accuracy of 84.73% which is pretty good performance.

DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection

Plant-leaf disease detection is one of the key problems of smart agriculture which has a significant impact on the global economy. To mitigate this, intelligent agricultural solutions are evolving that aid farmer to take preventive measures for improving crop production. With the advancement of deep learning, many convolutional neural network models have blazed their way to the identification of plant-leaf diseases. However, these models are limited to the detection of specific crops only. Therefore, this paper presents a new deeper lightweight convolutional neural network architecture (DLMC-Net) to perform plant leaf disease detection across multiple crops for real-time agricultural applications. In the proposed model, a sequence of collective blocks is introduced along with the passage layer to extract deep features. These benefits in feature propagation and feature reuse, which results in handling the vanishing gradient problem. Moreover, point-wise and separable convolution blocks are employed to reduce the number of trainable parameters. The efficacy of the proposed DLMC-Net model is validated across four publicly available datasets, namely citrus, cucumber, grapes, and tomato. Experimental results of the proposed model are compared against seven state-of-the-art models on eight parameters, namely accuracy, error, precision, recall, sensitivity, specificity, F1-score, and Matthews correlation coefficient. Experiments demonstrate that the proposed model has surpassed all the considered models, even under complex background conditions, with an accuracy of 93.56%, 92.34%, 99.50%, and 96.56% on citrus, cucumber, grapes, and tomato, respectively. Moreover, the proposed DLMC-Net requires only 6.4 million trainable parameters, which is the second best among the compared models. Therefore, it can be asserted that the proposed model is a viable alternative to perform plant leaf disease detection across multiple crops.

Lightweight convolutional neural network architecture design for music genre classification using evolutionary stochastic hyperparameter selection

AbstractConvolutional neural networks (CNNs) have succeeded in various domains, including music information retrieval (MIR). Music genre classification (MGC) is one such task in the MIR that has gained attention over the years because of the massive increase in online music content. Accurate indexing and automatic classification of these large volumes of music content require high computational resources, which pose a significant challenge to building a lightweight system. CNNs are a popular deep learning‐based choice for building systems for MGC. However, finding an optimal CNN architecture for MGC requires domain knowledge both in CNN architecture design and music. We present MGA‐CNN, a genetic algorithm‐based approach with a novel stochastic hyperparameter selection for finding an optimal lightweight CNN‐based architecture for the MGC task. The proposed approach is unique in automating the CNN architecture design for the MGC task. MGA‐CNN is evaluated on three widely used music datasets and compared with seven peer rivals, which include three automatic CNN architecture design approaches and four manually designed popular CNN architectures. The experimental results show that MGA‐CNN surpasses the peer approaches in terms of classification accuracy, parameter numbers, and execution time. The optimal architectures generated by MGA‐CNN also achieve classification accuracy comparable to the manually designed CNN architectures while spending fewer computing resources.