Plastic waste presents a growing environmental challenge due to its persistence and increasing accumulation, making efficient and accurate classification of plastic types–including PET, HDPE, PVC, LDPE, PP, PS, and others– essential for real-time automated recycling systems. The inherent heterogeneity of plastic types presents significant classification challenges that a single network cannot adequately address. In this work, we propose a class-aware multi-expert knowledge distillation framework to deploy high-capacity models on resource-constrained edge devices. Accordingly, specialized models have been developed, each serving as an ’expert’ for specific plastic classes. By leveraging these ensemble classifiers within a multi-expert teacher knowledge distillation framework, we have introduced a compact student model, further optimized into an even tinier variant suitable for real-time embedded deployment. The performance of such models is heavily contingent upon the quality of the training dataset. This challenge is particularly pronounced in plastic waste due to the high morphological variability of materials and their susceptibility to deformation or alterations in appearance caused by aging and contamination. Consequently, existing public datasets often lack the diversity required for robust model generalization. To address this limitation, we have consolidated multiple datasets into a unified framework, providing a valuable benchmark for automated plastic waste classification. The generalizability of the proposed framework has been demonstrated across multiple student and teacher models using both homogeneous and heterogeneous model backbone architectures. Experimental results demonstrate that the proposed distillation approach enables the tiny student to achieve high accuracy and robustness while significantly reducing model size (by 8.3) and inference time.

Plastic waste is becoming nowadays a huge environmental problem. Circular economy seems to be the best choice for preserving the environment, by reinserting plastic waste into the industrial process and use. A key step for that is the plastic waste segregation from other waste. This paper proposes a new plastic waste segregation approach based on two modalities: spectroscopy and optical images. The research handles an optical camera for waste image acquisition, while the ground truth for plastic type identification, with respect to the seven primary plastic types: PET, HDPE, LDPE, PP, PS, PVC, and Other, is established through spectroscopic analysis. Various deep learning models were trained and evaluated on a dataset of publicly available and custom-collected images, aiming to achieve human-level accuracy in distinguishing between plastic types. The proposed methodology is based on a set of steps, where the model is first built implicitly on a segregation of different wastes, and then applied via transfer learning for separating the seven plastic types. The system performances demonstrate the feasibility and effectiveness of this combined approach for accurate and efficient plastic waste sorting. The proposed system has been implemented on Raspberry Pi 4 Model B+ as the central processing unit, integrating computer vision and deep learning techniques for efficient and accurate sorting.

Visual examination of defects in the industry is a tedious task requiring great attention and time to scrutinize several parts. That’s why implementing an automated system for defect detection should be placed to mitigate such disruption in the production line. Such systems leverage several image processing techniques in order to identify and extract defect areas. Unfortunately, the similarity between defect types makes defect classification very ambiguous. To cope with such a limitation, possibility theory offers a modeling space where uncertainty and ambiguity can be conveniently handled. In this context, the Dubois and Prade fromalism is applied on texture feature’s regions containing defaults basing on Local Binary Pattern (LBP). Accordingly, a possibility mapping of the different defects, which yields a more reliable decision making within the possibility/necessity paradigm. This approach’s effectiveness is demonstrated through a comparative study against other state-of-the-art techniques. The proposed method outperforms others by achieving a mean Precision mP of 81.6% on the NEU-DET database.

Breast cancer early diagnosis is a major concern for reducing deadly cases. Automation of microcalcification detection is becoming increasingly important given their tiny scale. At this step, a high false negative rate is observed, leading to high ambiguity. In this context, conventional approaches are unable to handle such ambiguity. Possibility theory offers a powerful paradigm enabling to handle a high uncertainty level. Therefore, in this research,new possibilistic modeling strategy for microcalcification detection is proposed. The developed system is based on k-means clustering followed by a fusion of the corresponding possibility distributions, for decision making. For enhancing classification's accuracy, two aspects may be taken into consideration: scattering within classes and the class discrimination power. A high inter-class variance may be regarded as an evidence of good discrimination. Nevertheless, in case of existing highly scattered classes, miss-classifications of samples at the class margins are still encountered, even at high inter-class variance. Clustering solves this problem by redefining classes in a more compact way. Under this paradigm, the clustering optimization is performed using a criteria based on the Area Under Curve(AUC), where the confidence degree level is based on the consistency principle of Dubois and Prade. The above strategy has been applied for the detection of microcalcifications, based on a set of pyramidal and multi-resolution based features. Validation on the Digital Database for Screening Mammography (DDSM) public dataset has been undertaken. The proposed system, which gives 99.4 % detection accuracy, can be used to assist medical practitioners.

Dendritic cells can be seen as a mirror of our immune system. Based on their in virto analysis, biological experts are now able to study the impact of food contaminants on the human immune system. Accordingly, a visual characterization of dendritic cell morphology can provide an indirect estimation of the toxicity. In this paper, we propose an automatic classification of dendritic cells that could serve as a second non-subjective opinion for pathologists. The proposed approach is built on pre-processing steps for segmentation and cell detection in microscopic images. Then, a set of features such as shape descriptors are extracted for cell characterization. At this step, three cell classes are distinctively identified by experts. Nevertheless, a high ambiguity is revealed between cell classes. Possibility theory can offer a realistic framework for making reliable decisions under high ambiguity. It exploits a human natural concept of the implicit use of probability distribution for deciding on the possibility of some assertions in some contexts where a cognitive conflict is observed while interfering existing related postulates, leading to high ambiguity. Based on the consistency concept of Dubois and Prade, a transformation of the probability into a possibility distribution is undertaken. Under possibility paradigm, a further feature selection in the possibility space using the Shapely index. Compared to state-of-the art methods the proposed approach yielded on a real dataset of nearly 630 samples an improvement in terms of the mean precision rate, the Recall rate, and the F1-measure.

Microcalcifications are very tiny deposits of calcium allocated in the breast tissue. Their gray level is similar to the dense normal breast tissue so its very difficult to differentiate between them. Once detected, its very difficult to between malign end benign microcalcifications. In this paper, we apply a new method to extract features of microcalcifications in order to classify them into malign and benign. This technique, called the Discriminative Completed Local Binary Pattern (DisCLBP), extracts texture characteristics of breast tissue in order to characterize the severity of microcalcifications. Classification of these structures is accomplished through Artificial Neural Network (ANN), which separate them in two groups: malignant and benign microcalcifications. Performance results are given in terms of receiver operating characteristic (ROC). The area under curve (AUC) of the corresponding approach has been found to be 93.45%.

Microcalcifications are tiny deposits of calcium located in breast tissue. They appeared as very small highlighted regions in comparaison with their surrounding tissue. The difference of contrast between microcalcifications and the normal tissue depend on the breast density: The more the breast is dense, the less is the contrast. In this context, we propose to enhance microcalcifications details for each type of breast density using for methods. As we know that the BIRADS/ACR 4 contains dense breast, That's why we have proposed to make the Non Linear Stratching (NLS)automatic by applying an improved Tsallis entropy. The proposed mammography enhancement approach is evaluated on the Digital Database for Screening Mammography (DDSM) database.