Top-1 Accuracy Research Articles

Explainable artificial intelligence and microbiome data for food geographical origin: the Mozzarella di Bufala Campana PDO Case of Study.

Identifying the origin of a food product holds paramount importance in ensuring food safety, quality, and authenticity. Knowing where a food item comes from provides crucial information about its production methods, handling practices, and potential exposure to contaminants. Machine learning techniques play a pivotal role in this process by enabling the analysis of complex data sets to uncover patterns and associations that can reveal the geographical source of a food item. This study aims to investigate the potential use of explainable artificial intelligence for identifying the food origin. The case of study of Mozzarella di Bufala Campana PDO has been considered by examining the composition of the microbiota in each samples. Three different supervised machine learning algorithms have been compared and the best classifier model is represented by Random Forest with an Area Under the Curve (AUC) value of 0.93 and the top accuracy of 0.87. Machine learning models effectively classify origin, offering innovative ways to authenticate regional products and support local economies. Further research can explore microbiota analysis and extend applicability to diverse food products and contexts for enhanced accuracy and broader impact.

High Performance CNN Accelerators based on Hardware and Algorithm Co-Optimization

The efficacy of convolutional neural networks (CNNs) has led to their extensive application in picture recognition and categorization. Nevertheless, embedded systems face challenges when it comes to storing the massive volumes of weight data required by CNNs in their on-chip memory. Pruning a convolutional neural network (CNN) model can reduce its size with no impact on accuracy; however, when used with a parallel architecture, the resulting model will be slower to execute. A CNN compression method that is hardware-centric is introduced in this paper. A model of a deep neural network (DNN) will include "pruning layers (P-layers)" and "no-pruning layers (NP-layers)" attached to it. For effective and parallel processing, an NP-layer employs a regular distribution for its weights. Pruning causes a P-layer to have an irregular form, yet the high compression ratio it yields makes the shape undesirable. Using uniform and gradual quantization methods, we can achieve a processing efficiency/compression ratio trade-off with a small loss of precision. Integrating multiple parallel finite impulse response (FIR) filters into an NP-layer distributed convolutional architecture, we further improve the regular model. The P-layer irregular sparse model is suggested to use an ADF-based processing element. The suggested compression strategy and hardware architecture can be utilised by a hardware/algorithm co-optimization (HACO) method to run an NP→P hybrid compressed CNN model on FPGAs. The VGG-16 network achieves a compression ratio of 27.5× with a top-5 accuracy loss of 0.44% by using a hardware accelerator on a single FPGA chip without off-chip memory. A result of 83.0 FPS, or 1.8 times quicker, was achieved when the compressed VGG-16 model was implemented on a Xilinx VCU118 evaluation board for image applications. Index Terms - CNN, Accelerators, Co-Optimization.

Efficient filter pruning: Reducing model complexity through redundancy graph decomposition

Filter pruning has emerged as a crucial technique facilitating the efficient deployment of Convolutional Neural Networks (CNNs) on edge devices. The resultant lightweight models often present a challenging trade-off between performance and compression rate. Moreover, the excessive granularity of filter selection, coupled with intricate fine-tuning procedures, hampers the practical applicability of numerous pruning methodologies. To tackle these limitations, our paper introduces a novel pruning approach that initially constructs an undirected graph, effectively capturing convolutional layer redundancy by quantifying the similarity among output feature maps. Subsequently, our method iteratively eliminates highly redundant and low-value filters, employing both k-core decomposition and importance ranking criteria. In practical implementation, we adopt a one-shot pruning strategy, thereby minimizing the requirement for extensive fine-tuning. Comprehensive comparative experiments demonstrate the efficacy of our proposed approach in achieving remarkable improvements in model compression without compromising performance. For instance, with CIFAR-10, our method achieves state-of-the-art results on DenseNet-40 with a mere 70 fine-tuning epochs, while effectively removing 49.39% of FLOPs, resulting in a mere 0.01% accuracy loss. Furthermore, in the context of ImageNet, our approach excels by removing 48.81% of FLOPs and 23.71% of parameters from ResNet-50, exhibiting only 1.47% and 0.83% decline in Top-1 and Top-5 Accuracy respectively. The code is publicly available at https://github.com/lijiang99/redundancy-graph-decomposition.

Data-Efficient, Chemistry-Aware Machine Learning Predictions of Diels-Alder Reaction Outcomes.

The application of machine learning models to the prediction of reaction outcomes currently needs large and/or highly featurized data sets. We show that a chemistry-aware model, NERF, which mimics the bonding changes that occur during reactions, allows for highly accurate predictions of the outcomes of Diels-Alder reactions using a relatively small training set, with no pretraining and no additional features. We establish a diverse data set of 9537 intramolecular, hetero-, aromatic, and inverse electron demand Diels-Alder reactions. This data set is used to train a NERF model, and the performance is compared against state-of-the-art classification and generative machine learning models across low- and high-data regimes, with and without pretraining. The predictive accuracy (regio- and site selectivity in the major product) achieved by NERF exceeds 90% when as little as 40% of the data set is used for training. Another high-performing model, Chemformer, requires a larger training data set (>45%) and pretraining to reach 90% Top-1 accuracy. Accurate predictions of less-represented reaction subclasses, such as those involving heteroatomic or aromatic substrates, require higher percentages of training data. We also show how NERF can use small amounts of additional training data to quickly learn new systems and improve its overall understanding of reactivity. Synthetic chemists stand to benefit as this model can be rapidly expanded and tailored to areas of chemistry corresponding to the low-data regime.

Attention-Based Multi-Kernelized and Boundary-Aware Network for image semantic segmentation

Recent studies have witnessed the prosperity of image semantic segmentation brought by deep learning technology, including the integration of diverse network structures, attention mechanisms, and even boundary perception. Essentially, the main challenge of semantic segmentation lies in how to incorporate and align more information like contexts and details in the feature representation enabling the network to better distinguish and recognize objects in an image for improved semantic understanding. To this end, we propose a novel end-to-end deep framework termed Attention-Based Multi-Kernelized and Boundary-Aware Network to enhance the semantic segmentation performance. Specifically, the proposed network enables thoroughly exploring and engaging the multi-kernelized contextual information with high-frequency boundary information by applying novel modules of Multi-Kernelized Spatial Attention and Boundary-Aware Hybrid Attention, aiming to enhance the segmentation result with more comprehensive feature representation capability. Extensive experiments further consolidate the superiority of the proposed network with improved segmentation results with the mIoU of 51.1% and 82.9% on ADE20K and Cityscapes datasets, as well as classification results with Top-1 accuracy of 84.0% on ImageNet-1K dataset against competitive baselines.

A machine learning application for the automatic recognition of planktonic foraminifera in thin sections

The automated recognition of fossil planktonic foraminifera in thin section (TS) samples can bring benefit through reduction of time, costs, and human effort for biostratigraphic analyses. In this work we present the outcomes of an Eni internal Research and Development project aimed at evaluating the feasibility of automated recognition of microfossils by means of computer vision. We trained two Deep-Learning (DL) models to first identify fossil candidates within TS samples and subsequently classify selected taxa among 66 registered Oligocene-Miocene planktonic foraminifera species. In the step of fossil detection, we achieved a mean average precision of 81.4% in extracting bounding boxes containing fossils from raw TS photomicrographs. In the subsequent fossil classification step, we obtained a top-1 accuracy of 70% (i.e., in approximately 70% of cases, the label assigned by the biostratigraphers coincided with the model best guess) and a top-3 accuracy of 88% (i.e., in 88% of cases, it was included within the top-3 species predicted by the model). Given these promising performances, the fossil detection and classification pipeline has been deployed in a web application that indicates fossil-containing bounding boxes and suggests the top-3 most likely species for each sample. Biostratigraphers’ corrections/confirmations are stored to be used for future re-trainings and incrementally improve the performances of the algorithms.

Classification of Caries Based on CBCT: A Deep Learning Network Interpretability Study.

This study aimed to create a caries classification scheme based on cone-beam computed tomography (CBCT) and develop two deep learning models to improve caries classification accuracy. A total of 2713 axial slices were obtained from CBCT images of 204 carious teeth. Both classification models were trained and tested using the same pretrained classification networks on the dataset, including ResNet50_vd, MobileNetV3_large_ssld, and ResNet50_vd_ssld. The first model was used directly to classify the original images (direct classification model). The second model incorporated a presegmentation step for interpretation (interpretable classification model). Performance evaluation metrics including accuracy, precision, recall, and F1 score were calculated. The Local Interpretable Model-agnostic Explanations (LIME) method was employed to elucidate the decision-making process of the two models. In addition, a minimum distance between caries and pulp was introduced for determining the treatment strategies for type II carious teeth. The direct model that utilized the ResNet50_vd_ssld network achieved top accuracy, precision, recall, and F1 score of 0.700, 0.786, 0.606, and 0.616, respectively. Conversely, the interpretable model consistently yielded metrics surpassing 0.917, irrespective of the network employed. The LIME algorithm confirmed the interpretability of the classification models by identifying key image features for caries classification. Evaluation of treatment strategies for type II carious teeth revealed a significant negative correlation (p < 0.01) with the minimum distance. These results demonstrated that the CBCT-based caries classification scheme and the two classification models appeared to be acceptable tools for the diagnosis and categorization of dental caries.

Large-scale self-normalizing neural networks

Self-normalizing neural networks (SNN) regulate the activation and gradient flows through activation functions with the self-normalization property. As SNNs do not rely on norms computed from minibatches, they are more friendly to data parallelism, kernel fusion, and emerging architectures such as ReRAM-based accelerators. However, existing SNNs have mainly demonstrated their effectiveness on toy datasets and fall short in accuracy when dealing with large-scale tasks like ImageNet. They lack the strong normalization, regularization, and expression power required for wider, deeper models and larger-scale tasks. To enhance the normalization strength, this paper introduces a comprehensive and practical definition of the self-normalization property in terms of the stability and attractiveness of the statistical fixed points. It is comprehensive as it jointly considers all the fixed points used by existing studies: the first and second moment of forward activation and the expected Frobenius norm of backward gradient. The practicality comes from the analytical equations provided by our paper to assess the stability and attractiveness of each fixed point, which are derived from theoretical analysis of the forward and backward signals. The proposed definition is applied to a meta activation function inspired by prior research, leading to a stronger self-normalizing activation function named “bi-scaled exponential linear unit with backward standardized” (bSELU-BSTD). We provide both theoretical and empirical evidence to show that it is superior to existing studies. To enhance the regularization and expression power, we further propose scaled-Mixup and channel-wise scale & shift. With these three techniques, our approach achieves 75.23% top-1 accuracy on the ImageNet with Conv MobileNet V1, surpassing the performance of existing self-normalizing activation functions. To the best of our knowledge, this is the first SNN that achieves comparable accuracy to batch normalization on ImageNet.

Balanced quantum neural architecture search

In the last decade, there has been continuously increasing attention on Neural Architecture Search (NAS). The design of network architecture is aimed at automatically generating efficient neural networks in the absence of prior knowledge. Most of current oneshot NAS methods are based on weight sharing. However, there are two main problems that lead to suboptimal search results. Firstly, the large scale supernet results in a complex and difficult search for optimal subnetwork structures in the large discrete search space. Secondly, weight sharing makes the internal weight parameters of the supernet cannot always vary towards the final weights required for the optimal network structure due to the influence of different structures. These problems are responsible for the fact that the performance of the subnetworks searched by the supernet cannot represent the real performance of the subnetworks trained from scratch. To solve these problems, we propose the method based on quantum evolution and balance pool, called BQNAS, which consists of two stages. The single path supernet is trained based on weight sharing and balance pool sampling method. Then, quantum parallelism is exploited for one-hot encoding and searching for the optimal subnetwork structure based on quantum evolutionary algorithms. It can greatly improve the search efficiency of oneshot approaches in subnetwork sampling and evaluate the real performance for searching for superior subnetwork structure. Extensive experiments on two datasets show that the proposed approach outperforms the state-of-the-art ones. Specifically, BQNAS achieves a top-1 accuracy of 97.27% on CIFAR-10 and 81.36% on CIFAR-100.

Ancient mural dynasty recognition algorithm based on a neural network architecture search

A neural network model needs to be manually designed for ancient mural dynasty recognition, and this paper proposes an ancient mural dynasty recognition algorithm that is based on a neural architecture search (NAS). First, the structural edge information of mural images is extracted for use by the neural network model in recognizing mural missions. Second, an NAS algorithm that is based on contrast selection (CS) simplifies the architecture search to an incremental CS and then searches for the optimal network architecture on the mural dataset. Finally, the identified optimal network architecture is used for training and testing to complete the mural dynasty recognition task. The results show that the top accuracy of the proposed method on the mural dataset is 88.10%, the recall rate is 87.52%, and the precision rate is 87.69%. Each evaluation index used by the neural network model is superior to that of classical network models such as AlexNet and ResNet-50. Compared with NAS methods such as ASNG and MIGO, the accuracy of mural dynasty recognition is higher by an average of 4.27% when using the proposed method. The proposed method is verified on CIFAR-10, CIFAR-100, ImageNet16-120 and other datasets and achieves a good recognition accuracy in the NAS-bench-201 search space, which averages 93.26%, 70.73% and 45.34%, respectively, on the abovementioned datasets.

Lipwatch: Enabling Silent Speech Recognition on Smartwatches using Acoustic Sensing

Silent Speech Interfaces (SSI) on mobile devices offer a privacy-friendly alternative to conventional voice input methods. Previous research has primarily focused on smartphones. In this paper, we introduce Lipwatch, a novel system that utilizes acoustic sensing techniques to enable SSI on smartwatches. Lipwatch leverages the inaudible waves emitted by the watch's speaker to capture lip movements and then analyzes the echo to enable SSI. In contrast to acoustic sensing-based SSI on smartphones, our development of Lipwatch takes into full consideration the specific scenarios and requirements associated with smartwatches. Firstly, we elaborate a wake-up-free mechanism, allowing users to interact without the need for a wake-up phrase or button presses. The mechanism utilizes the inertial sensors on the smartwatch to detect gestures, in combination with acoustic signals that detecting lip movements to determine whether SSI should be activated. Secondly, we design a flexible silent speech recognition mechanism that explores limited vocabulary recognition to comprehend a broader range of user commands, even those not present in the training dataset, relieving users from strict adherence to predefined commands. We evaluate Lipwatch on 15 individuals using a set of the 80 most common interaction commands on smartwatches. The system achieves a Word Error Rate (WER) of 13.7% in user-independent test. Even when users utter commands containing words absent in the training set, Lipwatch still demonstrates a remarkable 88.7% top-3 accuracy. We implement a real-time version of Lipwatch on a commercial smartwatch. The user study shows that Lipwatch can be a practical and promising option to enable SSI on smartwatches.

InViTMixup: plant disease classification using convolutional vision transformer with Mixup augmentation

ABSTRACT Computer vision-based research is carried out to enhance the crop yield with automation solutions. In Tamil Nādu, tomato cultivation is carried out in many districts, including Coimbatore, and its contribution to the Indian economy, i.e. GDP growth, is significant. This paper proposes InViT Mixup, a novel approach toward image classification of diseased tomato leaves. The effectiveness of convolutional neural network (CNN) in obtaining spatial information and the attention mechanism of vision transformers, along with Mixup data augmentation, are combined, and the model’s generalization in predicting the diseased tomato based on leaf symptoms is evaluated. The proposed convolutional transformer model improvised the classification results of tomato disease in terms of accuracy and training speed compared to pure CNN or transformer-based models. In the proposed work, 10 tomato leaf disease classes from the PlantVillage dataset are used. Top-1 accuracy of 93.5% and Top-5 accuracy of 99.7% are achieved.

An Investigation on Hardware-Aware Vision Transformer Scaling

Vision Transformer (ViT) has demonstrated promising performance in various computer vision tasks, and recently attracted a lot of research attention. Many recent works have focused on proposing new architectures to improve ViT and deploying it into real-world applications. However, little effort has been made to analyze and understand ViT’s architecture design space and its implication for hardware costs on different devices. In this work, by simply scaling ViT’s depth, width, input size, and other basic configurations, we show that a scaled vanilla ViT model without bells and whistles can achieve comparable or superior accuracy-efficiency trade-off than most of the latest ViT variants. Specifically, compared with DeiT-Tiny, our scaled model achieves a ↑ 1.9% higher ImageNet top-1 accuracy under the same FLOPs and a ↑ 3.7% better ImageNet top-1 accuracy under the same latency on an NVIDIA Edge GPU TX2. Motivated by this, we further investigate the extracted scaling strategies from the following two aspects: (1) can these scaling strategies be transferred across different real hardware devices ? and (2) can these scaling strategies be transferred to different ViT variants and tasks ?. For (1), our exploration, based on various devices with different resource budgets, indicates that the transferability effectiveness depends on the underlying device together with its corresponding deployment tool. For (2), we validate the effective transferability of the aforementioned scaling strategies obtained from a vanilla ViT model on top of an image classification task to the PiT model, a strong ViT variant targeting efficiency as well as object detection and video classification tasks. In particular, when transferred to PiT, our scaling strategies lead to a boosted ImageNet top-1 accuracy of from 74.6% to 76.7% (↑ 2.1%) under the same 0.7G FLOPs. When transferred to the COCO object detection task, the average precision is boosted by ↑ 0.7% under a similar throughput on a V100 GPU.

An intelligent lithology recognition system for continental shale by using digital coring images and convolutional neural networks

Lithology identification is a critical component of reservoir evaluation and hydrocarbon development. However, the continental shale rocks, characterized by complex lithologies and developed laminations, present new challenges to traditional lithology identification techniques. In this study, we develop a centimeter-level intelligent identification system for continental shale lithology, leveraging digital image preprocessing and deep learning methods. Specifically, we construct a dataset from 400 m of drilling cores, encompassing 24 types of lithology and 152 lithological transitions. The downsampling and oversampling methods are employed to balance the dataset. The digital image denoising and restoration techniques are then utilized to enhance rock image features. Different neural network models are examined to obtain the optimum predictive results. The results indicate that data balancing improves the TOP1 and TOP5 accuracies by 10% and 5%, respectively. Gaussian low-pass filter denoising, in comparison to other methods, increases the TOP1 and TOP5 accuracies by 2%, establishing it as the superior denoising technique for lithology recognition. Considering both computational efficiency and prediction accuracy, EfficientNet emerges as the most effective model for continental shale image recognition. The optimized model attains up to 60% TOP1 and 95% TOP5 across 22 categories in drilling core samples. This study delves into the intelligent identification of macroscopic drilling core images of continental shale, and the findings can offer technical support for the interpretation and evaluation of continental shale reservoirs.

EPK-CLIP: External and Priori Knowledge CLIP for action recognition

Contrastive Language-Image Pretraining (CLIP) models have achieved significant success and have markedly improved the performance of various downstream tasks, including action recognition. However, how to effectively introduce knowledge into the field of action recognition remains an open question. In this work, an External and Priori Knowledge CLIP (EPK-CLIP) is proposed to introduce external knowledge into the model. To capture external knowledge, an external knowledge embedding module is proposed, which can generate and utilize human-object interaction relations as external knowledge, enabling the model to learn better features. Furthermore, the sparse regularization is introduced in the loss function, endowing the model with the ability to exploit the sparse priori knowledge inherent in the classification task. Finally, multiple inference module is proposed to obtain classification results from both direct and indirect perspectives. Specifically, the final classification result is obtained by fusing the outputs of different reasoning modules. Moreover, four external knowledge datasets: Kinetics-400-VC, Jester-VC, HMDB-51-VC, and UCF-101-VC are built and released for public usage, which is a multimodal extension of corresponding action datasets respectively. Under fully-supervised settings, our model achieves the top-1 accuracy of 84.3%, 97.1%, 82.9%, and 98.2% on Kinetics-400, Jester, HMDB-51, and UCF-101, respectively. In zero-shot experiments, our model also achieves state-of-the-art results, with top-1 accuracy of 51.6% and 77.7% on HMDB-51 and UCF-101, respectively. All related datasets and code can be found at https://github.com/geek12138/EPK-CLIP.

Application of deep learning in isolated tooth identification

BackgroundTeeth identification has a pivotal role in the dental curriculum and provides one of the important foundations of clinical practice. Accurately identifying teeth is a vital aspect of dental education and clinical practice, but can be challenging due to the anatomical similarities between categories. In this study, we aim to explore the possibility of using a deep learning model to classify isolated tooth by a set of photographs.MethodsA collection of 5,100 photographs from 850 isolated human tooth specimens were assembled to serve as the dataset for this study. Each tooth was carefully labeled during the data collection phase through direct observation. We developed a deep learning model that incorporates the state-of-the-art feature extractor and attention mechanism to classify each tooth based on a set of 6 photographs captured from multiple angles. To increase the validity of model evaluation, a voting-based strategy was applied to refine the test set to generate a more reliable label, and the model was evaluated under different types of classification granularities.ResultsThis deep learning model achieved top-3 accuracies of over 90% in all classification types, with an average AUC of 0.95. The Cohen’s Kappa demonstrated good agreement between model prediction and the test set.ConclusionsThis deep learning model can achieve performance comparable to that of human experts and has the potential to become a valuable tool for dental education and various applications in accurately identifying isolated tooth.

INTERPRETABLE AND GENERALIZABLE HTS CODE CLASSIFICATION FRAMEWORK

The automation of Harmonized Tariff Schedule (HTS) code classification - a crucial task in international trade that entails assigning standardized codes to goods for tariff and regulatory purposes - is examined in this paper using GPT-3, a cutting-edge Large Language Model (LLM). The focus on creative prompt-engineering techniques and the usage a frozen model approach eliminates the need for further model fine-tuning. The suggested approach makes use of GPT-3’s built-in capabilities to decipher intricate product descriptions and reliably assign them to the appropriate HTS categories. We show through an array of experiments that strategic prompt adjustments including few-shot prompting allowed the model's initial top-1 accuracy of 23 percent to be significantly increased to an average of 73 percent. The main strengths of the approach are the explainability for the chosen category as well as the fully generalizable nature of the methodology.

MAFormer: A transformer network with multi-scale attention fusion for visual recognition

Vision Transformer and its variants have demonstrated great potential in various computer vision tasks. However conventional vision transformers often focus on global dependency at a coarse level, which results in a learning challenge on global relationships and fine-grained representation at a token level. In this paper, we introduce Multi-scale Attention Fusion into transformer (MAFormer), which explores local aggregation and global feature extraction in a dual-stream framework for visual recognition. We develop a simple but effective module to explore the full potential of transformers for visual representation by learning fine-grained and coarse-grained features at a token level and dynamically fusing them. Our Multi-scale Attention Fusion (MAF) block consists of: i) a local window attention branch that learns short-range interactions within windows, aggregating fine-grained local features; ii) global feature extraction through a novel Global Learning with Down-sampling (GLD) operation to efficiently capture long-range context information within the whole image; iii) a fusion module that self-explores the integration of both features via attention. Our MAFormer achieves state-of-the-art results on several common vision tasks. In particular, MAFormer-L achieves 85.9% Top-1 accuracy on ImageNet, surpassing CSWin-B and LV-ViT-L by 1.7% and 0.6% respectively. On MSCOCO, MAFormer outperforms the prior art CSWin by 1.7% mAPs on object detection and 1.4% on instance segmentation with similar-sized parameters. With the performance, MAFormer demonstrates the ability to generalize across various visual benchmarks and prospects as a general backbone for different self-supervised pre-training tasks in the future.

Clustering-Guided Twin Contrastive Learning for Endomicroscopy Image Classification.

Learning better representations is essential in medical image analysis for computer-aided diagnosis. However, learning discriminative semantic features is a major challenge due to the lack of large-scale well-annotated datasets. Thus, how can we learn a well-structured categorizable embedding space in limited-scale and unlabeled datasets? In this paper, we proposed a novel clustering-guided twin-contrastive learning framework (CTCL) that learns the discriminative representations of probe-based confocal laser endomicroscopy (pCLE) images for gastrointestinal (GI) tumor classification. Compared with traditional contrastive learning, in which only two randomly augmented views of the same instance are considered, the proposed CTCL aligns more semantically related and class-consistent samples by clustering, which improved intra-class tightness and inter-class variability to produce more informative representations. Furthermore, based on the inherent properties of CLE (geometric invariance and intrinsic noise), we proposed to regard CLE images with any angle rotation and CLE images with different noises as the same instance, respectively, for increased variability and diversity of samples. By optimizing CTCL in an end-to-end expectation-maximization framework, comprehensive experimental results demonstrated that CTCL-based visual representations achieved competitive performance on each downstream task as well as more robustness and transferability compared with existing state-of-the-art SSL and supervised methods. Notably, CTCL achieved 75.60%/78.45% and 64.12%/77.37% top-1 accuracy on the linear evaluation protocol and few-shot classification downstream tasks, respectively, which outperformed the previous best results by 1.27%/1.63% and 0.5%/3%, respectively. The proposed method holds great potential to assist pathologists in achieving an automated, fast, and high-precision diagnosis of GI tumors and accurately determining different stages of tumor development based on CLE images.

Enhancing Skin Cancer Classification using Efficient Net B0-B7 through Convolutional Neural Networks and Transfer Learning with Patient-Specific Data.

Skin cancer diagnosis challenges dermatologists due to its complex visual variations across diagnostic categories. Convolutional neural networks (CNNs), specifically the Efficient Net B0-B7 series, have shown superiority in multiclass skin cancer classification. This study addresses the limitations of visual examination by presenting a tailored preprocessing pipeline designed for Efficient Net models. Leveraging transfer learning with pre-trained ImageNet weights, the research aims to enhance diagnostic accuracy in an imbalanced multiclass classification context. The study develops a specialized image preprocessing pipeline involving image scaling, dataset augmentation, and artifact removal tailored to the nuances of Efficient Net models. Using the Efficient Net B0-B7 dataset, transfer learning fine-tunes CNNs with pre-trained ImageNet weights. Rigorous evaluation employs key metrics like Precision, Recall, Accuracy, F1 Score, and Confusion Matrices to assess the impact of transfer learning and fine-tuning on each Efficient Net variant's performance in classifying diverse skin cancer categories. The research showcases the effectiveness of the tailored preprocessing pipeline for Efficient Net models. Transfer learning and fine-tuning significantly enhance the models' ability to discern diverse skin cancer categories. The evaluation of eight Efficient Net models (B0-B7) for skin cancer classification reveals distinct performance patterns across various cancer classes. While the majority class, Benign Kertosis, achieves high accuracy (>87%), challenges arise in accurately classifying Eczema classes. Melanoma, despite its minority representation (2.42% of images), attains an average accuracy of 80.51% across all models. However, suboptimal performance is observed in predicting warts molluscum (90.7%) and psoriasis (84.2%) instances, highlighting the need for targeted improvements in accurately identifying specific skin cancer types. The study on skin cancer classification utilizes EfficientNets B0-B7 with transfer learning from ImageNet weights. The pinnacle performance is observed with EfficientNet-B7, achieving a groundbreaking top-1 accuracy of 84.4% and top-5 accuracy of 97.1%. Remarkably efficient, it is 8.4 times smaller than the leading CNN. Detailed per-class classification exactitudes through Confusion Matrices affirm its proficiency, signaling the potential of EfficientNets for precise dermatological image analysis.